Theses and Dissertations

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Open Access
Beam asymmetry in the reaction channel γp→ηΔ + at Glue X
(Faculty of Graduate Studies and Research, University of Regina, 2024-08) Neelamana, Varun; Papandreou, Zisis; Lolos, George; Huber, Garth; Stevens, Justin R.; McBeth, Joyce; Watts, Dan
Photoproduction mechanisms studied in the GlueX experiment allows the mapping of light mesons in unprecedented detail with particular interest in exotic meson candidates. This is achieved by impinging an 8.2-8.8 GeV linearly polarized photon beam on a liquid hydrogen target. The measurement of beam asymmetry Σ will help constrain quasi-particle t-channel exchange processes using Regge theory. Understanding the photoproduction exchange mechanisms is a crucial ingredient in establishing hybrid and exotic photoproduced light meson states. Σ is extracted from the azimuthal angular distribution between the meson production plane and the polarized photon beam. In particular, we will report results on the beam asymmetry measurements for η in the reaction p →η Δ+. This reaction with a recoiling Δ+ will allow for comparison and validation of theoretical calculations and provide additional validation of the η asymmetry with a recoiling proton. The different isospin of the Δ+ imposes additional restrictions that further constrain allowed Regge exchanges. The results were similar to η-proton i.e Σ ≈ 1 but showed a deviation from theoretical models of the η - Δ+ especially towards higher t values. This may help guide modifications to these models for production and exchange processes involving η meson.
Open Access
The role of modular construction and BIM technologies in sustainable construction and demolition waste management
(Faculty of Graduate Studies and Research, University of Regina, 2024-08) Naghibalsadati, Farzin; Ng, Kelvin Tsun Wai; Wu, Peng
Construction and demolition activities significantly contribute to global waste generation, necessitating sustainable measures. This thesis explores advanced C&DW management through Building Information Modeling (BIM) and modular construction techniques. In the initial phase of the study, a comprehensive text-mining analysis of 493 scholarly publications (2009-2024) reveals key themes and temporal trends. The cooccurrence analysis identified three distinct clusters centered on C&DW management, highlighting strong correlations between "sustainability" (Links=41), "BIM" (Links=46), and "C&DW" (Links=46). Thematic development and evolution analysis indicated that during the third period (2018-2021), transversal themes included Material Passport (OCC=92) and Prefabrication (OCC=482). In the fourth period (2022-2024), transversal themes encompassed Digital Twin (OCC=44), Waste Minimization approaches in BIM (OCC=64), and Decision-Making Systems (OCC=64). Strategic diagrams and Temporal evolution mapping generated by SciMAT software illustrate the progressive integration of BIM tools like digital twins, Material Passport, Prefabrication, and Decision-Making System, offering avenues to optimize waste reduction. The subsequent part of the study explores modular construction techniques for their potential to mitigate material waste and enhance sustainability in building practices. Significant research interest since 2015, coinciding with the UN Sustainable Development Goals (SDGs), is observed. Keyword trends have sustained interest in recycling since 2014. Cluster and network analysis highlight "Recycled Aggregate Concrete (RAC)" as a high-impact theme (confidence 100%).The importance of the mechanical properties of RAC in modular construction is also emphasized.
Open Access
Advanced CNN architecture integrating machine learning algorithms for precise Alzheimer's disease classification
(Faculty of Graduate Studies and Research, University of Regina, 2024-08) Mollazadeh, Shima; Torabi, Farshid; Tontiwachwuthikul, Paitoon (P.T.); Idem, Raphael
Alzheimer's disease (AD) is a degenerative neurological disorder that affects millions of individuals worldwide and is very difficult to detect and treat in its early stages. This thesis presents a novel architecture for a convolutional neural network (CNN) designed exclusively to classify Alzheimer's disease using functional magnetic resonance imaging (fMRI) data. This work improves the accuracy and reliability of early Alzheimer's identification by using state-of-the-art deep learning techniques to the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. The basis of this research is the ADNI dataset, a vast collection of brain imaging and associated data from people with different degrees of cognitive impairment. The primary objectives are to classify Alzheimer's disease into distinct categories using cognitively normal (CN), early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI), and Alzheimer's disease (AD) using the recently developed CNN architecture. This study also uses transfer learning techniques to compare the performance of the new CNN with well-known deep learning models like ResNet50 and VGG16, as well as with more conventional machine learning algorithms like XG Boost, k-nearest neighbor (KNN), and Random Forest. The innovative CNN architecture is meticulously designed to maximize classification accuracy. The preprocessing steps involve resizing fMRI images to 109x91 pixels and labeling them accordingly. The network incorporates convolution layers with 3x3 kernels, ReLU activation functions, and 2x2 pooling layers, transforming the images into feature vectors that are subsequently classified. Compared to previous tested models, the innovative CNN architecture performed better, achieving an impressive 99.51% classification accuracy. In terms of comparison analysis, the accuracy of the VGG16 model was 98.24%, whereas the accuracy of the ResNet50 model was 96.05%. The XG Boost classifier, combined with VGG16 for feature extraction, reached an accuracy of 96.93%. The KNN algorithm, also paired with VGG16, exhibited outstanding performance with an accuracy of 98.68%, making it the most effective among the traditional machine learning methods tested. With VGG16 included, the Random Forest classifier produced an accuracy of 94.70%. The outcomes demonstrate how well the suggested CNN architecture performs in comparison to current deep learning and machine learning models in precisely classifying Alzheimer's disease stages. This study demonstrates how sophisticated CNN designs and transfer learning can be used to enhance Alzheimer's disease early detection and diagnosis. The findings suggest that further exploration of alternative deep learning networks, such as convolutional auto encoders, Alex Net, and Google Net, as well as ensemble methods, could enhance model generalization and minimize overfitting. In conclusion, this thesis presents a significant advancement in Alzheimer’s disease classification using fMRI data, providing a robust framework for future research and development in neuroimaging and deep learning applications. The superior performance of the novel CNN architecture demonstrates its potential as a valuable tool for early diagnosis, which is crucial for managing and potentially mitigating the way Alzheimer's disease advances.
Open Access
Investigative studies on the stability of an amine blend in the presence of exhaust gas dust (metal oxide) impurities during an amine-based CO2 capture process
(Faculty of Graduate Studies and Research, University of Regina, 2024-08) Boakye, Thomas; Idem, Raphael; Supap, Teeradet; Ibrahim, Hussameldin
This research work investigates the degradation kinetics of MEA/DMAE bi-blend solvent, with a focus on the influence of temperature, oxygen concentration, and type and amount of exhaust gas metal oxides. Utilizing a combination of experimental approaches and kinetic modeling, this study was used to provide a comprehensive analysis of the factors that affect MEA/DMAE stability and degradation rates. This research commenced first, by determining the solubility of various dominant iron and steel flue gas metal oxides, namely, Fe2O3, ZnO, MnO, and Al2O3. The oxides were dissolved in a 200 ml, 5M, and 0.30 mol CO2/mol bi-blend of MEA/DMAE solvent. In decreasing order, the solubility results for ZnO, Fe2O3, MnO, and Al2O3 were 387.51 ppm, 15.96 ppm, 4.57 ppm, and 3.43 ppm, respectively. By flowing oxygen at different concentrations (balance nitrogen) through a 200 ml volume filtrate of the generated metal oxide dissolved amine solvent in a three-necked flask exposed to different absorber temperatures, a continuous 21-day lab-scale degradation experiment was carried out. Fe2O3 had the greatest influence on the degradation of a CO2-loaded bi-blend of MEA/DMAE among the metal oxides taken into consideration, followed by ZnO all based on amine degradation results in mmol/hr, accumulated amount of ammonia emissions in ppmV, and ammonia emissions rate in ppmV/hr. Based solely on which one had the most degrading effect, Fe2O3 in the concentrations of 15.96 ppm, 11.97 ppm, and 7.98 ppm was selected and used against varying temperatures (in the range of 40 oC, 50 oC, and 60 oC) and oxygen concentrations (ranging from 6%, 12%, and 18%) to explore their effects on degradation rates and ammonia emission rates. A kinetic model was developed for the DMAE degradation rate and MEA degradation rate with activation energies of 50,495.13 J/mol, and 60,310.9 J/mol respectively. The order of reactions obtained from the kinetic analysis was 1.22 and 0.98 for DMAE and MEA respectively. The results showed that DMAE degraded at a faster rate than MEA. The studies also showed an increasing trend in the rate of MEA/DMAE degradation and the rate of ammonia emissions with increasing oxygen concentration and operating temperature. A high activation energy for MEA implies that more energy (temperature) was needed to degrade MEA relative to DMAE, which had a lower activation energy. A lower order of reaction for oxygen for MEA, also implies that the impact of oxygen on MEA degradation is less than its impact on DMAE degradation. Fe2O3 therefore has a higher catalytic effect on MEA/DMAE degradation implying that upon contact with the amine solvent, the amine has a high tendency to degrade at a faster rate, causing an increase in solvent losses and an increase in the cost of solvent replacement. Further implications include corrosion, clogging, and the degraded amine's fouling of columns and piping.
Open Access
Development of a pellet extruder with co-axial nozzle for 3D printing using inflatable extrudates
(Faculty of Graduate Studies and Research, University of Regina, 2024-08) Habib, Md Ahsanul; Khondoker, Mohammad; Muthu, SD Jacob; Peng, Wei
Additive manufacturing (AM) has emerged as one of the core components of the fourth industrial revolution, Industry 4.0. Among others, the extrusion AM (EAM) of thermoplastic materials has been named as the most widely adopted technology. Fused filament fabrication (FFF) relies on the commercial availability of expensive filaments; hence pellet extruder-based EAM techniques are desired. Large-format EAM systems would benefit from the ability to print lightweight objects with less materials and lower power consumption which can be possible by using hollow extrudates rather than solid extrudates to print objects. In this work, we designed a custom extruder head and developed an EAM system that allows the extrusion of inflatable hollow extrudates of a relatively wide material choice. By incorporating a co-axial nozzle-needle system, a thermoplastic shell was extruded while the hollow core was generated by using pressurized Nitrogen gas. The ability to print using hollow extrudates with controllable inflation allows printing objects with gradient part density with different degrees of mechanical properties. In this article, the effect of different process parameters namely, extrusion temperature, extrusion speed, and gas pressure were studied using poly-lactic acid (PLA) pellets. Initially, a set of preliminary tests was conducted to identify the maximum and minimum ranges of these parameters that result in consistent hollow extrudates. Later, the parameters were varied to understand how they affect the core diameter and shell thickness of the hollow extrudates. These findings were supported by analyses of microscopic images taken under an optical microscope. In the next phase of our experiment, we printed an inflated cylindrical part using the process parameters derived from the initial set of experiments. We carefully compared the results with the data obtained earlier to ensure accuracy and consistency. Finally, we successfully printed an object with varying densities in different sections. Keywords: Additive Manufacturing; Extrusion Additive Manufacturing; Hollow Extrudates; Pellet Extrusion; Fused Filament Fabrication.
Open Access
Measurement of the pion exclusive electro-production cross-section in the E12-19-006 experiment in Hall-C at Jefferson Lab
(Faculty of Graduate Studies and Research, University of Regina, 2024-09) Kumar, Vijay; Huber, Garth; Mobed, Nader; Barbi, Mauricio; Mack, David; Fallat, Shaun; Ireland, David
One of the most effective methods for exploring the transition from hadronic de- grees of freedom to quark-gluon degrees of freedom in Quantum Chromodynamics (QCD) is through the investigation of \exclusive" pion and kaon electro-production reactions at various Q2 and -t values. The E12-19-006 experiment is conducted within the confines of experimental Hall C at the Thomas Jefferson National Accel- erator Facility, USA, for such studies. The primary aim of the experiment is to first enhance our comprehension of the pion electro-production cross-section and its form factor at Q2 = 0.38 and 0.42 GeV2. This is the first run period of the E12-19-006 experiment which ran in summer 2019. A more profound understanding of the pion electro-production reaction, 1H(e,e'π+)n, at low Q2 is deemed essential to employ this electro-production reaction (an indirect technique) for the high Q2 studies, thereby delving deeper into the realm of QCD. Consequently, this dissertation presents a thorough analysis of the experimental data acquired in the first run period of the E12-19-006 experiment. In pursuit of precision, a series of systematic studies (target boiling correction study, the elastic reaction cross-section measurements, study for determining vari- ous kinematics offsets, etc.) are conducted to discern the accuracy of the analyzed data, a prerequisite for the use of Rosenbluth separation technique to separate the pion electro-production cross-section terms in t bins. The separated pion electro- production cross-section through the Rosenbluth separation technique is then used to extract the pion electromagnetic form factor. In this dissertation, the pion electro-production cross-section is carefully dissected into its four constituent components: longitudinal (𝜎L), transverse (σT ), longitudinal- transverse (σLT ), and transverse-transverse (σTT ), using the full version of Rosenbluth separation technique for the Q2 = 0.38 GeV2. The technique is simultaneously fitted to the unseparated pion electro-production cross-sections at the three values of polar- ization of the virtual photon (ϵ), i.e., ϵ = 0.286, 0.629 and 0.781. An iterative process is applied to refine the parameters of the model cross-sections until the yield ratio of experimental and Monte Carlo simulation converges. In this study, 21 iterations are conducted to refine the model cross-section parameters. The final pion electro- production cross-section terms are then determined for 7 t bins using the optimized parameters of the model cross-sections.
Open Access
Real-time Evaluation of an Automated Computer Vision System to Monitor Pain Behaviour in Older Adults
(Faculty of Graduate Studies and Research, University of Regina, 2024-09) Stopyn, Rhonda Jennifer Nicole; Hadjistavropoulos, Thomas; Asmundson, Gordon; Gallant, Natasha; Taati, Babak; Paranjape, Raman; Jutai, Jeffrey W.
A large body of literature supports the systematic observation of facial expressions as a tool for assessing pain in both younger and older adults. Such observation is especially critical for older adults who have limited ability to communicate their pain experience due to dementia. While frequent monitoring of pain behaviours in dementia is constrained by resource limitations, computer vision technology has the potential to mitigate these challenges, especially in long-term care environments where many people with severe dementia reside. A computerized algorithm designed to assess pain behaviour in older adults with and without dementia was recently developed and validated against video recorded images. The algorithm was incorporated within an automated system that provided alerts when facial pain expressions were detected. This study conducted the first live, real-time evaluation of the automated pain behaviour detection system with community-dwelling older adults in a laboratory setting. Testing involved a total of 65 participants completing three safely-administered experimentally-induced pain tasks using a thermal pain stimulator. A video camera was used to facilitate recording and automatic processing of facial activity. Pain behaviour detection occurred when systemgenerated pain intensity scores of the facial expressions displayed by participants exceeded a predetermined threshold score. When the incidence of facial pain expression occurred, an electronic notification (e.g., email and a signal light) was generated as notifications of pain behaviour detection. Participants completed continuous self-report pain intensity ratings during the thermal pain tasks. Receiver Operating Characteristic curve analyses were used to determine the sensitivity and specificity of the system in identifying pain- and non-pain facial expressions using gold standard manual coding completed by trained coders. Gender differences were also explored in relation to system performance. Correlational procedures were used to evaluate the relationship between pain intensity scores generated by the system, continuous self-report pain ratings, observational pain coding, and stimulus temperatures. This study supported the potential viability of the automated pain behaviour detection system in correctly identifying live, real-time instances of facial pain expressions in older adults. System-generated pain behaviour scoring achieved a maximal greater correlation with gold standard manual coding compared to prior testing using video-recordings. Specifically, system performance improved when more intense facial pain expressiveness was considered compared to more subtle facial expressions at lower pain intensities. In comparing system scoring to manual coding, there was not a one-to-one correspondence in coding but a range of comparative values that varied from participant to participant. Correlational analyses showed that continuous self-report pain ratings were weakly correlated with system scoring and manual coding. While average pain scores remained homogenous across genders, results suggested that the system performed better at identifying pain expressions for women compared to men. As expected, the pain-related facial movements of brow lowering and levator contraction were unique predictors of system-generated scores. Future evaluations of the system involving field trials in long-term care settings with older clinical populations would further elucidate the performance of the system. This technology is expected to aid in the assessment of pain in people living with dementia while addressing resource constraints in long-term care environments and reduce burden for caregivers. Keywords: Pain, aging, technology, older adults, computer vision, dementia
Open Access
Machine learning-based models for failure prediction and propagation in smart grid systems
(Faculty of Graduate Studies and Research, University of Regina, 2024-09) Salehpour, Ali; Al-Anbagi, Irfan; Bais, Abdul; Wang, Zhanle (Gerald); Yow, Kin-Choong; Louafi, Habib; Ameli, Amir
The smart grid connects components of power systems and communication networks in an interdependent two-way system that supplies or receives electricity to or from prosumers and collects data that enables it to react to usage levels and interference from threats, such as cyber-attacks. Cascading failures resulting from cyberattacks are one of the main concerns in smart grid systems. The use of artificial intelligence (AI)-based algorithms has become more relevant in identifying and forecasting such cascading failures. However, existing models that study the propagation of cascading failures either omit the impact of the communication network or power characteristics on the propagation process. To address this gap, in this thesis, we propose a set of novel cyber-attack failure propagation models in smart grids. First, our realistic failure propagation (RFProp) model addresses the system’s heterogeneity by assigning different roles to its components. We define rules and interdependencies for failure propagation and propose a new model for studying cascading failures. In addition, the RFProp graph-based model identifies the most vulnerable nodes and implements power flow analysis to guarantee that all transmission lines work below capacity and remove lines exceeding capacity. Our results establish that by considering both power and communication characteristics and interdependencies, cascading failures are modeled more accurately. In the second step, we propose a novel earlystage failure prediction (ESFP) model based on supervised machine learning (ML) algorithms. We use the RFProp model to generate a dataset for training these algorithms and predicting the state of a system’s components after a failure propagates in that system. Using the ESFP model, we predict failures of all of a system’s elements in the early stages of failure propagation. We use the XGBoost algorithm and consider the features of both the power and communication networks that provide high accuracy in the prediction process for failures. We also identify the location of the initial failures, as this allows for further protection plans and decisions. In the third step, we use the real-time digital simulator (RTDS) to develop a real-time early-stage failure prediction (RESP) model that simulates the power system in real time and makes it more realistic. We evaluate the RESP model’s effectiveness using the IEEE 14-bus system, which results in the XGBoost algorithm achieving a high accuracy in predicting attacks and with a lower testing time. Finally, we introduce a real-time attack prediction (RTAP) model based on a real-time testbed designed to examine the impact of cyber-attacks on smart grid systems. We utilize real-time simulators, including RTDS and network simulator 3 (NS3) to emulate the behavior of power and communication networks. Using this model, we employ various ML algorithms to detect cyber-attacks. We evaluate the effectiveness of the proposed model using an IEEE 14-bus test case, demonstrating high accuracy and efficient testing time.
Open Access
Mathematical modeling and simulation of the performance of potassium glycinate in CO2 absorption in a packed-bed absorption column
(Faculty of Graduate Studies and Research, University of Regina, 2024-09) Domfeh, Adjei; Idem, Raphael; Torabi, Farshid; Ibrahim, Hussameldin; Supap, Teeradet
The aim of this research undertaking was to develop a mathematical model representation for the capture of CO2 using potassium glycinate as the absorption solvent. To this end, the study was subdivided into three major parts each designed to generate the requisite data for the subsequent stage. The three major parts of the study included the development of industrial process simulation to ascertain the emission data and characteristics of flue gas emanating from different industrial processes. The main processes under study were, Power Generation with particular emphasis on the Combined Cycle Gas Turbine setup, Natural Gas Pre-treatment, where the simulations were developed for gas dehydration, chilling and Natural Gas Liquid (NGL) recovery, and the Acid Gas Removal (AGR) Modules, Cement Manufacturing with emphasis on the Pyroprocessing stage and finally Iron and Steel Production, where simulations were built for such production stages as the Raw material Sintering, Pelletization, Coke Production, Pig Iron Production, and the Basic Oxygen Furnace setup. The emission data from each of these process industries were collected and used for the sizing of an absorption tower which then became the basis for the hydrodynamic solution. The Absorber model developed in Aspen Hysys based on the flowrates and CO2 partial pressures in each flue gas stream served as the template to generating a Computer Aided Design (CAD) version of the column as a flow channel facilitating the process of resolving the hydrodynamic solution. In the second section of the study, the hydrodynamic solution of the absorption model was solved based on the physicochemical properties of potassium glycinate particularly density and viscosity at 6M and 60 ºC after a thorough assessment of the flow behaviour dynamics of the solvent was performed and the results was contrasted with MEA which is the bench mark solvent in CO2 post combustion capture. The final phase of the study investigated the mass transfer with reaction aspect of the interaction of CO2 and potassium glycinate at varying CO2 concentrations to understand its impact on capture processes. The solution; based on a stated rate expression; −𝑟𝐶𝑂2 = 7.5 × 10−1 𝑒(−6.7×102 𝑇)𝐶𝑠 0.11 𝐶 𝐶𝑂2 1.14 reveal that the reaction rate of CO2 increases from 0.0022 kg/m³·s at 5% CO2 to 0.0027 kg/m³·s at 10% CO2, stabilizes at 0.0026 kg/m³·s at 15% CO2, and remains constant at 0.0027 kg/m³·s from 20% CO2 onwards. In contrast, potassium glycinate's reaction rate increases from 0.0058 kg/m³·s at 5% CO2 to 0.0068 kg/m³·s at 10% CO2, remains steady at 0.0068 kg/m³·s up to 20% CO2, and slightly rises to 0.0069 kg/m³·s at 30% and 40% CO2. The initial rise in CO2 reaction rates suggests enhanced efficiency with increasing CO2 concentration, while the plateau indicates a saturation point. Potassium glycinate shows improved absorption capacity and reaction efficiency up to a steady state, with minimal gains at higher concentrations. These trends imply that potassium glycinate remains effective across a broad range of CO2 concentrations, crucial for optimizing CO2 capture systems.
Open Access
Stability studies of a novel amine blend for the capture of CO2 generated from indirect co-combustion of natural gas and biomass
(Faculty of Graduate Studies and Research, University of Regina, 2024-09) Asante, Raymond Owiredu; Idem, Raphael; Supap, Teeradet; Tontiwachwuthikul, Paitoon (P.T.)
The excessive release of CO2 from human activities has led to global warming and climate change, becoming a critical global issue for many years. Data released by the Environment Protection Agency of United States in 2022 clearly shows that, CO2 is the most emitted gas among other greenhouse gases. In response, numerous strategies have been developed to address this pressing problem. One particularly compelling approach is to use an optimal mix of fuels in combination with post-combustion capture technology to achieve net-zero or even negative CO2 emissions. Over the past years, solvent absorption in post-combustion capture technology has shown significant reliability and efficiency in reducing CO2 emissions. Despite this, investors face major challenges such as the cost of solvents, solvent loss, and the expenses involved in solvent regeneration. Monoethanolamine (MEA) is the most extensively studied alkanolamine absorbent and serves as the benchmark for evaluating other absorbents. MEA is recognized for its high absorption rate, low cost, and low viscosity. However, MEA is prone to a high degradation rate, which leads to significant solvent loss. In the field of CO2 capture, degradation refers to the diminished capacity of the solvent to effectively capture CO2 as expected during the capture process. This degradation is primarily caused by undesirable side reactions between the absorbent and impurities in the flue gas, such as oxygen, NOx, SOx, particulate matter, and heavy metals. Degradation is an endothermic reaction so the process is favoured at high temperature in the presence of these flue gas impurities. Solvent degradation is an important parameter to consider during solvent selection for commercial use in CO2 capture process. It does not only lead to solvent loss but degradation products like carboxylic acids and heat stable salt (HSS) promotes corrosion. Also, nitrosamines emitted due to some absorbent reaction with NOx poses threat to human health. There are chances of fouling and solvent foaming due to degradation. Least to mention is the increase in cost of operation due to increase in heat duty required for regeneration because of the presence of degradation products. This work demonstrates the stability of 4M AMP:1-(2 HE) PRLD. The novel amine blend was subjected to some conditions mimicking CO2 capture for flue gas generated from indirect cocombustion of natural gas and biomass. When it was compared with a known amine blend, thus, 5M MEA:DMAE under same conditions, 4M AMP:1-(2 HE) PRLD, proved to be inherently more stable. It had a 33% increase in its rate in degradation as against 80% increment when the oxygen partial pressure was doubled keeping all other conditions constant. This implies the novel amine blend is stable and can be used commercially for the capture of CO2 from pretreated flue gas. The results highlight its resilience and potential for long-term application in environments where oxygen levels may fluctuate without succumbing easily to the degradation processes that typically affect similar compounds. A kinetic model was generated for the rate of degradation and it is of the form: r( hr M )=6.8222×10 9 e(− 8.314×T 50314.08 )[O₂] 1.3
Open Access
A mixed methods study on barriers and facilitators to exercise for suicidal ideation management
(Faculty of Graduate Studies and Research, University of Regina, 2024-10) Vig, Kelsey Danielle; Asmundson, Gordon; Hadjistavropoulos, Heather; Hadjistavropoulos, Thomas; Totosy, Julia; Gibb, Brandon E.
Suicide is a leading cause of premature death. Innovative and effective interventions are needed to prevent suicide deaths. Randomized controlled trials (RCTs) have demonstrated that a variety of structured exercise programs (e.g., aerobic exercise, resistance training exercise) improve mental health, including reducing anxiety and depressive symptoms. Moreover, failure to meet established guidelines for physical activity is associated with increased odds of experiencing suicidal behaviours. Exercise may, therefore, be one intervention option to reduce the suicidal behaviours (i.e., suicidal ideation [SI] and plans for suicide) that often precede suicide. In order to benefit from the effects of exercise, individuals with suicidal ideation must perceive exercise as an accessible, acceptable, and effective treatment option, otherwise they are unlikely to initiate and sustain an exercise program. This mixed-methods dissertation includes two studies that explored how individuals with SI perceive and experience exercise, with an emphasis on identifying facilitators and barriers to exercise. In Study 1, grounded theory methods were used to analyze data from semi-structured interviews with 17 adult Canadian participants with past-month SI. The overall theory derived from Study 1 suggests that exercise for individuals with SI is complex and should be tailored to each individual. This theory is made up of a core category of individualization, as well as four key concepts that relate to three major categories. The four key concepts of the theory are that facilitators and barriers to exercise (a) have individualized weights/impacts on exercise decisions, (b) are cumulative, interactive, and opposing forces, (c) are dynamic, and (d) exist on a spectrum from internal to external. The three major categories included in the study theory are (a) the cognitive-behavioural cycle, (b) priorities, values, and identity, and (c) interpersonal factors. In Study 2, 261 Canadian adult participants with past-month SI completed an online survey. The survey included measures of suicidal behaviour, facilitators and barriers to exercise (open-ended and closed-ended questions), past-week physical activity, and demographic and health questions. Due to the exploratory nature of the study, quantitative analyses were restricted to descriptive statistics. The qualitative and quantitative results of Study 2 supported and added to the theory developed in Study 1, including offering additional evidence of the core category, the four key concepts, and the three major categories. Most participants thought exercise can reduce SI. Improved health, both mental and physical, was a commonly reported motivator to exercise, and poor mental health was also a commonly reported barrier to exercise. Overall, the results of both studies demonstrated the importance of individualization when it comes to exercise for individuals with SI. Exercise may or may not be an accessible, acceptable, and/or effective intervention for any given individual with SI. The results may be used by clinicians, researchers, policy makers, and advocacy groups considering whether exercise might be an intervention option for individuals with SI. The results may assist future researchers who endeavor to develop exercise-based interventions for individuals with SI by providing a theoretical framework to guide intervention development and study planning (e.g., by highlighting the need to anticipate and address individual and fluctuating facilitators and barriers). Keywords: suicidal ideation, exercise, physical activity, facilitators and barriers to exercise, exercise adherence, grounded theory
Open Access
Enriched model categories and the Dold-Kan correspondence
(Faculty of Graduate Studies and Research, University of Regina, 2024-10) Ngopnang Ngompe, Arnaud; Frankland, Martin; Stanley, Donald; Fallat, Shaun; Herman, Allen; Zilles, Sandra; Ponto, Kate
The work we present in this thesis is an application of the monoidal properties of the Dold–Kan correspondence and is constituted of two main parts. In the first one, we observe that by a theorem of Christensen and Hovey, the category of nonnegatively graded chain complexes of left R-modules has a model structure, called the Hurewicz model structure, where the weak equivalences are the chain homotopy equivalences. Hence, the Dold–Kan correspondence induces a model structure on the category of simplicial left R-modules and some properties, notably it is monoidal. In the second part, we observe that changing the enrichment of an enriched, tensored and cotensored category along the Dold–Kan correspondence does not preserve the tensoring nor the cotensoring. Thus, we generalize this observation to any weak monoidal Quillen adjunction and we give an insight of which properties are preserved and which are weakened after changing the enrichment of an enriched model category along a right weak monoidal Quillen adjoint.
Open Access
Fabrication of PLA-hemp 3D printing filaments
(Faculty of Graduate Studies and Research, University of Regina, 2024-10) Uddin, Md. Nasir; Stilling, Denise; Khondoker, Mohammad; Mehrandezh, Mehran
This thesis focuses on the development and evaluation of polylactic acid (PLA) composites infused with hemp fibers for 3D printing applications. The project aims to leverage the intrinsic properties of hemp fibers and PLA to create a material that mitigates brittleness and enhances biodegradability while maintaining mechanical performance and printability. The methodology involves fabricating composite filaments from 2 mm PLA pellets and 0.4 mm hemp fiber particles. Hemp fiber at varying weight ratios (5 wt%, 10 wt%, and 15 wt%) was extruded using a single screw extruder. The optimal ratio was determined among these blends for maximum tensile and flexural strength, and the structures on the fractured surfaces were observed under low magnification. The filaments were studied under flexural and tensile mechanical testing, optical microscopy analysis, Fourier Transform Infrared thermal profiling, moisture absorption, and biodegradability analysis. The blends were compared with a commercial PLA filament for 3D printing applications. As a summary of the results, the composition of 5 wt% and 10 wt% hemp powder had lower Ultimate Tensile Strength (UTS) than 100 wt% PLA filaments; however, this blend had higher elongation and toughness. The highest tensile strength of 35 MPa occurred with the 95 wt% PLA-5 wt% hemp fiber composition. Similarly, the flexural strength for the 95 wt% PLA-5 wt% hemp fiber composition was 86 MPa, which was the highest among the composites, but lower than 100 wt% PLA. This composition showed a higher flexural modulus of 4 GPa which was greater than 100 wt% PLA. For water absorption, all the composite filaments showed the greatest rate of absorption during the first 1 hour. After the initial hour, no notable changes occurred. The 85 wt% PLA-15 wt% hemp fiber had the highest absorption rate; indicating that increasing the fiber percentage increases the water absorption. The biodegradability was studied using an enzyme-rich FABRICATION OF 3D PRINTING FILAMENTS M. UDDIN detergent which showed that 90 wt% PLA-10 wt% hemp fiber had the highest weight loss percentage across all three concentrations which was over 20% which indicates that this composition was more prone to biodegradability using this method than the other compositions. The printability of the filaments was analyzed qualitatively using a commercial 3D printer. A simple design for a “paper clip” was printed which requires both tensile and flexural strength. The products were printed with ease whereby the filaments melted and flowed through nozzles of 0.8 mm diameter for fused filament printing.
Open Access
Develop innovative methodology to optimally fill in missing values and predict progression on multiple sclerosis
(Faculty of Graduate Studies and Research, University of Regina, 2024-11) Pilehvari, Shima; Peng, Wei; Shirif, Ezeddin; Khan, Sharfuddin; Fan, Lisa; Bui, Francis
Applying Machine Learning (ML) to predict and track Multiple Sclerosis (MS) progression is a significant advancement in medical research, with the potential to enhance patient outcomes. Accurate MS prediction enables personalized treatment, timely interventions, and improved quality of life by slowing disease progression and preventing complications. This research aims to deepen our understanding of MS by developing ML models and comprehensive risk assessments to support early prognosis, guide treatment strategies, and reduce disease impact. A major challenge in medical research, especially in predicting MS progression, is effectively managing missing data in MS datasets. This study introduces an innovative sequential Multi-Imputation (MI) bootstrapping method to address the challenge of missing data in MS datasets. Initially, several ML algorithms, including k-Nearest Neighbors (kNN), Random Forest (RF), and Multilayer Perceptron (MLP), are evaluated for imputation efficiency. RF and MLP perform best, achieving overall accuracies of 92% and 91.5%, respectively, in handling missing data more accurately than other models. Given the effectiveness of RF and MLP in capturing complex patterns in data, these models are selected for further development. The next step applies Multi-Imputation (MI) bootstrapping in a sequential manner, prioritizing features based on the strength of their relationships, as determined by Pearson correlation analysis. This statistical technique identifies features with the highest correlations, ensuring that attributes with stronger relationships with other attributes, are imputed first. These imputed features then inform the next imputation in the sequence, cooperating with the subsequent ranked feature in the order. Bootstrapping, a resampling technique that involves replacement, creates multiple training datasets by repeatedly sampling from the original data, enhancing the robustness of the imputation process. The proposed sequential imputation method integrates bootstrapping with RF, achieving an accuracy up to 97 % for MS datasets. This iterative approach effectively imputes missing data attributes while accounting for feature significance and relationships. The results also show that prioritizing normalization improves scaling impact, and that the significant features in the original dataset are crucial to the accuracy of MS missing data estimations. These findings provide valuable insights into effective imputation techniques for MS prediction, offering a foundation for future improvements in handling missing data in specific datasets. In addition, this study solves the common overfitting problem caused by data imbalance through a comprehensive method combining feature extraction, undersampling, Synthetic Minority Oversampling Technique (SMOTE) and optimal threshold method. Support Vector Machine (SVM), Logistic Regression (LogR), Decision Tree (DT), RF, KNN, MLP and Naive Bayes (NB) are used for prognostic modeling while examining risk factor associations. The results showed that the proposed method prevented overfitting during model training and developed a robust MS progression prognosis model, achieving a prediction accuracy of 98%, particularly for SVM and MLP The methods proposed in this dissertation can help develop more concise guidelines for the medical research communities and improve their evaluation processes. These innovations not only advance prognostic analysis in MS, but also pave the way for future research focused on optimizing patient outcomes and treatment strategies.
Open Access
The sustainability of Saskatchewan municipalities
(Faculty of Graduate Studies and Research, University of Regina, 2024-11) Nadeau, Jean-Marc; McNutt, Kathleen; Farney, Jim; Rayner, Jeremy; Magnan, André; Rounce, Andrea
Although the municipal government is considered closest to the people, municipalities are not referenced in the Canadian constitution. Recognizing this gap, soon after joining the Confederation of Canada in 1905, Saskatchewan enacted a municipal act to regulate its rural and urban sectors. The province sought to ensure that citizens in urban municipalities were provided with municipal services such as water and sewage treatment, medical services, and recreation and that rural municipalities served agricultural land with roads, bridges, and other necessary infrastructure. The critical difference between urban and rural municipalities lies in population density, governance structure, and the types of services and infrastructure provided. Urban municipalities serve more densely populated areas with a range of services, while rural municipalities serve less populated, often agricultural regions focusing on maintaining roads and supporting agriculture. That said, in their role in supporting agriculture and industry, urban municipalities became regional economic hubs. With technological advancements in the agriculture industry and urbanization rates, the average farm size went from 160 acres under the 1872 Dominion Lands Claim Act to approximately 1,700 acres by 2021. The result has left many communities with shrinking populations. Roughly 166 of the existing 775 communities in Saskatchewan have more than 1,000 people, while 130 have less than 100 people. How sustainable are communities with such small populations? Given that the municipal legislative framework belongs to the provincial government, the research questions in this project seek to investigate why the provincial government refused to consolidate municipal governments despite data-supported arguments, including the recent Taskforce on Legislative Renewal led by Dr. Joe Garcea in 2000. This research aims to understand why the provincial government has forced the amalgamation of the school board system but has yet to restructure municipal governments despite previous attempts. This study develops a single case study using qualitative methods to analyze municipal governments in Saskatchewan. I collected and analyzed interviews with current and past municipal leaders, examining the data against the veto players' power theory developed by Dr. George Tsebelis, which employs historical institutionalism for its explanatory power to illustrate the resistance to change. The interview data are analyzed using the NVivo analytical platform, which allowed me to produce trend lines based on assigned attributes and codes. A total of 40 interviews were conducted and analyzed. During the data analysis phase of this project, several trends emerged. Veto players, such as the government of Saskatchewan and municipal leaders, have generally been far apart in their respective public policy positions regarding amalgamations. Municipal government leaders have historically demonstrated a strong status quo bias, resisted change, and, over time, produced path-dependent institutional processes. Municipal leaders have recognized that, eventually, there will be a need for amalgamations, but a bottom-up approach must drive this process. Three significant themes surfaced during the analysis of the data collected. First, the influence of historical institutional patterns leads decision-making processes down a path-dependent trajectory. A second theme based upon historical institutional introduced institutional change inertia by introducing status quo biases. Third, because municipal government leaders have varying opinions about amalgamation, MLAs are concerned about disturbing their voting base by forcing any modernization of the municipal sector. As the constitutional veto player governing the municipal sector, the provincial government will only foster sector modernization by incentivizing incremental change.
Open Access
Classifying men who perpetrate intimate partner violence: A 50-year systematic review and a new typology applicable to case management
(Faculty of Graduate Studies and Research, University of Regina, 2024-11) Giesbrecht, Crystal Joy; Bruer, Kaila; Keown, Leslie Anne; Jones, Nick; Vaughan, Adam; Hilton, Zoe; Scott, Katreena
This dissertation includes two studies: a systematic review of typologies of perpetrators of intimate partner violence (IPV) and a new typology of men who perpetrated IPV created using assessment data collected with the Service Planning Instrument (SPIn™). The systematic review included 177 typologies contained in 201 articles published between 1974 and 2024. Typologies in the review comprised: 1) family-only and generally violent; 2) family-only, generally violent, and borderline/dysphoric; 3) family-only, generally violent, low-level antisocial, and borderline/dysphoric; 4) severity and frequency of violence; 5) reactive and instrumental, 6) situational couple violence and coercive control; 7) personality types; 8) other typologies (e.g., treatment responsivity, physiological reactivity); and 9) perpetrators of intimate partner femicide. These typologies are summarized and compared, and findings from studies that examined recidivism and treatment outcomes by typology are reported. The new typology was created using data from 7,781 men in Alberta, Canada, who had been identified as having perpetrated IPV using the SPIn. Men in the sample were classified using seven indicator variables linked to general and IPV recidivism in empirical research and available in the SPIn: criminal history, failure while on conditions, violations of protection or no-contact orders, procriminal attitudes, antisocial peers, social/cognitive skills, and aggression/violence. The resulting typology included three classes: High Criminal History—High Antisocial Attitudes (18.5%; n = 1,439), High Criminal History—Low Antisocial Attitudes (51.6%; n = 4,015), and Low Criminal History—Low Antisocial Attitudes (29.9%; n = 2,327). Both classes with high criminal history report a greater prevalence of static variables relating to criminal history; the most notable difference between these two types is that the High Criminal History—High Antisocial Attitudes class scores high on variables relating to antisocial attitudes, whereas the High Criminal History—Low Antisocial Attitudes class does not. Individuals in the Low Criminal History—Low Antisocial Attitudes class have a low probability of all seven indicator variables. The three classes were compared on external variables linked to general and IPV recidivism (including history of violence, substance misuse, childhood trauma, mental health conditions, homicidal ideation, and employment problems). The High Criminal History—High Antisocial Attitudes class displayed the highest prevalence of all external variables (i.e., additional risk factors), the Low Criminal History—Low Antisocial Attitudes class had the lowest rates, and the High Criminal History—Low Antisocial Attitudes class scored intermediate to the other two classes. The three classes were also compared on four dichotomous measures of reoffending (any recidivism, technical violations, new non-violent offence, and new violent offence) at both one and three years. The High Criminal History—High Antisocial Attitudes class displayed the highest rate of recidivism on all four measures. The High Criminal History—Low Antisocial Attitudes class had a slightly lower prevalence than the High Criminal History—High Antisocial Attitudes class on all recidivism measures. The Low Criminal History—Low Antisocial Attitudes class had low rates of all forms of recidivism. Given the distinct differences between the three classes in terms of static and dynamic risk factors (i.e., criminogenic needs) and risk for reoffending, this typology is expected to have clinical utility for case management with men who have perpetrated IPV. Recommendations for risk management (e.g., supervision) and risk reduction (e.g., treatment/intervention programs) are discussed. Keywords: intimate partner violence, domestic violence, typology, perpetrators, systematic review, latent class analysis
Open Access
Integrating stewardship and resource recovery: A dual-faceted analysis of e-waste and used oil management practices of Canadian provinces
(Faculty of Graduate Studies and Research, University of Regina, 2024-11) Tasnim, Anica; Ng, Kelvin Tsun Wai; Veawab, Amornvadee (Amy)
Canada faces significant challenges in waste management, driven by high per capita waste generation. To address these issues, the country has implemented various waste management stewardship programs aimed at improving waste collection and resource recovery. This study examines the crucial role of stewardship in managing e-waste and used automotive resources including used oil, filters and containers. By focusing on stewardship practices, it highlights how effective management can improve collection rates, enhance resource recovery, and strengthen financial performance. The analysis emphasizes the importance of stewardship approaches to handling these special waste types, illustrating their potential to reduce environmental impact while optimizing resource use across Canadian provinces. The first part presents a comprehensive analysis of e-waste collection and management trends across six Canadian provinces, focusing on e-waste collection rates, provincial stewardship model attributes, program strategies and budget allocations from 2013 to 2020. Temporal and regression analyses were conducted using data from Electronic Product Recycling Association reports. The analysis emphasizes the significant impact of stewardship model attributes on e-waste collection rates, with Quebec emerging as a standout case, showcasing a remarkable 61.5% surge in collection rates. Findings from group analysis reveal a positive correlation between per capita e-waste collection rate and the growth of businesses and collection sites in Western Canada. This highlights the potential benefits of a coordinated waste management approach, emphasizing the importance of shared resources and collaborative policies. Financial aspects of e-waste management are also explored, revealing opportunities for improvement in Saskatchewan and Manitoba, where average allocations to e-waste collection efficiency stand at 6.6% and 7%, respectively. A 40.5% decrease in e-waste collection rates was observed in British Columbia, indicating additional public awareness campaigns may be required, as an 8% decline in consumer outreach was observed during the study period. The first part recommends leveraging region-specific needs to establish a collaborative approach, enhancing e-waste collection efforts. The second part addresses a gap in evaluating the recovery management systems for used oil, filters, and containers. The performance of resources recovery was examined in four Canadian provinces from 2010 to 2022 within automobile industry. The collection rates of resources, financial performance, and temporal changes of two original indicators: Resource Recovery Per Vehicle (RRPV), and Expenses Per Vehicle (EXPV) were examined. British Columbia and Quebec had the highest collection rates of used oil, filters, and containers (mean ranging 83.0 to 92.9%). Despite having the lowest mean collection rate of used oil (71.0%) and filters (78.7%), Saskatchewan has significant RRPV for used oil (20.4 liters) and filters (2.12 units). Decreasing RRPV (-0.01 to -0.38) trends were identified in all jurisdictions, suggesting the need for targeted recovery strategies towards automotive sectors. A mild increasing trend of EXPV in all jurisdictions is observed (slope +0.02 to +0.08). Quebec exhibited the most efficient resource recovery, with EXPV ranging from CAD 2.4 to CAD 3.3 per unit vehicle. Profit margin analysis revealed consistently high margins of 8.6% in Quebec, contrasting with Manitoba's lower 1.32%. The lower profit margin may partly be due to higher administrative costs (16.2%). The findings highlight the potential benefits of the proposed RRPV and EXPV indicators in evaluating management systems for used oil, filters, and containers.
Open Access
Screening of polyamine solvents for co2 capture: Solubility measurement and modeling
(Faculty of Graduate Studies and Research, University of Regina, 2024-11) Zeinali, Fatemeh; Henni, Amr; Ibrahim, Hussameldin; Peng, Wei
This research aims to identify new promising amines suitable for industrial-scale CO2 capture from natural and flue gas streams. Ideal amines should exhibit high CO2 solubility and low regeneration energy requirements. The amines selected for this study feature multiple amino groups, including combinations of secondary and tertiary amino groups within their molecular structures. The solubilities of two amines in CO2 were examined in this work: 1-[Bis[3-(dimethyl-amino) propyl]amino]2-propanol (BDMAPAP) and N, N, N', N', N''-Pentamethyl diethylene-triamine (PMDETA). The pressure decay method was used to measure and determine the solubilities. To fully assess the performance of these amines under varied circumstances, experiments were carried out at two temperatures (313.15K and 333.15K) with 10 wt.% and 30 wt.% concentrations and within a range of CO2 partial pressures. Both amines' CO2 uptake findings were compared to those of other amine solutions under comparable circumstances, such as piperazine (PZ), 1-ethylpiperazine (1-EPZ), 1-(2-Hydroxy-ethyl)piperazine (HEP), and 1,4-Bis(3-aminopropyl)piperazine. The results showed that compared to these other amines, there was a greater uptake of CO2. N, N, N', N'-tetramethyl-trimethylenediamine (TMTMDA), N, N-dimethyl-1,3-propane-diamine (DMPDA), N, N-dimethyl-dipropylene triamine (DMDPTA), 3,3'-Diamino-N-methyl-dipropylamine (DAMDPA), and 3,3'-Iminobis(N, N-dimethylpropylamine) (IBDMPA) were also compared to see how these amines were absorbed at 313.15 K. Except IBDMPA; the results revealed noticeably greater CO2 uptakes for the amines under comparable circumstances. Notably, the CO2 uptakes were substantially higher than benchmark amines commonly used in the industry, such as piperazine (PZ) and monoethanolamine (MEA). The superior performance of these amines indicates a high potential for more efficient CO2 capture processes. The model employed is the electrolyte Non-Random Two-Liquids (eNRTL) in conjunction with the Redlich-Kwong equation of state for the gas phase. PMDETA's average absolute deviation (AAD%) between the experimental data and the estimated values was 0.1%, 0.01%, and 0.008% for mole fractions, temperatures, and pressures, respectively. Similarly, for (BDMAPAP), the %AAD between the eNRTL model's estimated values and experimental data amounted to 0.15%, 0.03%, and 0.30% for mole fractions, pressures, and temperatures, respectively. The GLE model facilitated the calculation of the molar heat of absorption using the Gibbs-Helmholtz equation. The examined amines exhibited lower molar heat than other amines, such as PZ, MEA, and DMAPA, offering critical insights into their thermal efficiency and energy requirements during regeneration. The molar absorption heat at infinite dilution of CO2 was approximately -43 kJ·mol⁻¹ for both amine solutions, which is significantly lower than that of other amines, including PZ and MEA. In conclusion, the high CO2 solubility and favorable thermodynamic properties of BDMAPAP and PMDETA highlight their promise as efficient CO2 capture agents. These findings suggest that the selected amines could significantly enhance industrial CO2 capture processes, offering a potential pathway to more sustainable and cost-effective solutions for mitigating CO2 emissions.
Open Access
The impact of 3D printing on traditional construction supply chains: Challenges, benefits, and a proposed framework
(Faculty of Graduate Studies and Research, University of Regina, 2024-11) Sultana, Shahanaj; Khondoker, Mohammad; Khan, Sharfuddin; Kabir, Golam
The construction supply chain is surrounded by various challenges which hinder its effectiveness. The increasing need for housing due to the fast-growing population in North America is a matter of concern for decision makers. 3D printing or additive manufacturing is an emerging technology that is being considered as a potential solution to housing issues. The purpose of this study is to highlight the existing challenges encountered by traditional construction supply chains and the potential advantages that 3D printing, or specifically 3D concrete printing, can offer in overcoming these challenges with the help of mitigation strategies. In order to achieve this goal, a systematic review is carried out. This review revealed a total of eleven (11) obstacles concerning material, cost, and environment. Moreover, eight (8) benefits of 3D concrete printing were identified from various sources of literature. Literature also provided the groundwork for linking these challenges with the functions of the construction supply chain and the benefits that result from applying 3D printing. Expert validation for the challenges and benefits is achieved through the employment of exploratory factor analysis. Furthermore, an attempt has been made to show the structural relationship between the findings using interpretative structural modelling and the MICMAC method. This relational hierarchy has aided in identifying the most significant challenges and benefits to address. Finally, a framework based on literature has been proposed to demonstrate how these challenges, functions and benefits can interact within the construction supply chain. In this framework, mitigation strategies have been suggested to assist decision makers mitigate the impacts of these challenges on the functions of the construction supply chain. Keywords: 3D Concrete Printing; Additive Manufacturing; 3D Printing; Construction Supply Chain; Challenges; Impacts, PRISMA, Systematic Literature Review, Interpretative Structural Modeling.
Open Access
Structural analysis and fatigue prediction of harrow tines used in Canadian prairies
(Faculty of Graduate Studies and Research, University of Regina, 2024-11) Rahman, Arafater; Khondoker, Mohammad; Kabir, Golam; Khan, Sharfuddin
Canadian prairies are renowned for their agricultural contribution to the global food market, where harrow tine is a critical component of agricultural equipment used for soil preparation and weed control before crop cultivation. Unfortunately, during operation, these tines are exposed to repetitive cyclic loading, which eventually causes fatigue failure. Commercially available three different harrow tines named 5-1895, 5-2709, 5-2776 undergo an experimental fatigue evaluation and are validated through a numerical approach using Finite Element Analysis (FEA). After that, fatigue life estimation for different deflections under different circumstances in the real field was determined where 5-2676 showed ground-breaking life compared with others. The results of the study showed that the fatigue life is highly dependent on geometry, number of coils, pitch angle, leg length, and coil diameter. 0.354 HT model resembles 5-2776 developed to investigate the effect of wire diameter. For this reason, the experimental SN curve for ASTM A229, Class I was developed utilizing the fatigue bending test. Following this, a comparative study between six harrow tine models was analyzed based on their harrowing ability against identical deflections. This study also identified critical locations of stress concentration, which could be used to optimize the design of the tines to improve their fatigue life. Experimental fractured surfaces went through morphological investigation. This research has an impeccable impact on prairies’ agricultural acceleration by saving time and unpredictable fatigue failure often faced by farmers through designing more reliable and durable harrow tines, which can reduce agricultural maintenance costs and increase efficiency. Keywords: Fatgue failure, Harrow Tine, life estimation, S-N curve, ASTM A229.