oURspace
University of Regina Institutional Repository
The mission of the oURspace digital repository is to share and preserve the scholarly, creative, and cultural work produced at the University of Regina.
What are some of the benefits of depositing your works in oURspace?
- Increased access to your scholarly publications.
- Content is indexed and discoverable in Google Scholar.
- Compliance with open access funding requirements.
- Long term preservation of your work.
Please contact ourspace@uregina.ca if you have questions or want more information about oURspace.
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Recent Submissions
Possibilities and Challenges of Learning English through Virtual Speaking Clubs
(2023-09) Fuhrmann, Lucrécia Raquel; Sterzuk, Andrea; de Melo, Ana Beatriz Ruiz
The COVID-19 pandemic has made online language learning and interaction more prevalent. In this
paper, we investigate English language learners' experiences in a virtual speaking club hosted by one
of the co-authors. The main objective is to explore English learners' experience in speaking clubs. The
study uses online focus groups to explore learner experiences, focusing on feelings accompanying the
language learning process. Findings suggest that virtual speaking clubs are cost-saving and time-
efficient options for learners. Furthermore, the clubs provide opportunities for greater learner
participation and interaction than traditional spaces, as they are a way to share social resources,
benefits, and power.
Metal culvert renewal prioritization framework development: A study for Saskatchewan Highways
(Faculty of Graduate Studies and Research, University of Regina, 2023-08) Marzbani, Pooneh; Kabir, Golam; Khan, Sharfuddin Ahmed; Khondoker, Mohammad
Saskatchewan comprises over 250,000 km (160,000 mi) of roads, the highest length of road surface compared to any other Canadian province. Along these roads, over 26,500 culverts have been installed for passaging water flow. Some of which have been installed over a century ago with no recorded installation dates. The failure of culverts, caused by a wide range of factors such as environmental, structural, or material failures, can result in sudden and catastrophic consequences, leading to injuries or loss of life. Additionally, damaged culverts can negatively impact water quality by causing erosion and scour, as well as impeding the passage of aquatic species between their habitats upstream and downstream. Thus, timely replacement of culverts can play a crucial role in minimizing such damages.
This study evaluates three methods, namely, ordinal logistic regression, artificial neural network, and Fuzzy Inference System, for the prioritization of culverts for renewal. Specifically, the approach presented in this research is developed for metal pipe materials, which are extensively used in culvert installations. Using the condition of 1,000 metal culverts located along Saskatchewan highways, the three methods were employed to rank their condition and determine their renewal time. The evaluation of model performance was conducted using a range of established metrics including the area under the ROC (Receiver Operating characteristic) curve, percentage of correct predictions (PCP), confusion matrix, accuracy, precision, recall, and F1 score. The results of the study indicate that the artificial neural network optimized by genetic algorithm outperforms the other two methods, providing the most effective approach for culvert renewal prioritization.
Keywords: Culvert renewal; Ordinal Logistic Regression; Artificial Neural Network; Fuzzy Inference System.
NHL draft gem mining: Use of the impact score to find Successful NHL players
(Faculty of Graduate Studies and Research, University of Regina, 2023-03) Watson, Alexander Clarence Russell; Hoeber, Larena; Reimer, Harold; Pitariu, Adrian
Less than 48% of NHL Draftees play in a single NHL game (Wilson, 2016). This percentage further decreases when looking at the players selected after the first round of the draft (Tingling et al., 2011). The objective of this study was to determine if the impact score can accurately predict NHL success for forwards and defensemen selected after the first round, when it comes to games played and points scored. The impact score is a percentage analytic that I created, that measures the player’s points per game total relative to the points scored by their teammates. Data was collected using HockeyDB and EliteProspects (hockey statistic databases) for players selected in the 2005-2015 NHL Entry Drafts. Data was then analyzed to see if having a higher impact score results in more NHL career games played, more minutes played per game, and more points per minute played. A regression was used to answer these questions for players drafted in round 1 and rounds 2-7, controlling for yearly age, league group, area of development, and position. It was found that the addition of the impact score offered significant changes in variation for career games played, time on ice per game, and points per time on ice, but this was more prominent for individuals selected in round 1. It was also found that there was no significant interaction between the impact score and position in both round 1 and rounds 2-7.
Frequency domain analysis of U-Net segmented ultrasound images
(Faculty of Graduate Studies and Research, University of Regina, 2023-08) Steenbock, Henrik Reimer; Zilles, Sandra; Yang, Xue-Dong; Teymurazyan, Aram
During prostate cancer brachytherapy, catheters are inserted into a patient's
prostate for a highly localized radiation treatment. Accurately placed catheters are
critical for successful treatment and ultrasound images are taken throughout the procedure
to verify their exact positions. However, manually locating catheters on ultrasound
images is extremely di cult, time consuming, and happens while the catheters
are still in the patient. A fully automatic solution could signi cantly reduce procedure
time and potentially even improve the precision. This thesis introduces a novel
approach that segments 2D ultrasound images using the successful U-Net architecture
to determine catheter candidates. These candidates are then extracted and Fourier
Transformed into the frequency domain. De-convolution is performed directly in the
frequency domain to reconstruct a number of frequency coe cients and remove noise.
Additional features are calculated from the frequency coe cients to supplement the
determined U-Net con dence and candidate location. Altogether, the features from
each catheter candidate are classi ed by AdaBoost.
Establishing wastewater surveillance for SARS-CoV-2 in Southern Saskatchewan
(Faculty of Graduate Studies and Research, University of Regina, 2023-06) Reuter, Sonja Tamara; Hansmeier, Nicole; Chao, Tzu-Chiao; Yost, Chistopher; Van Hamme, Jonathan
Wastewater surveillance has become a crucial part in the monitoring of the COVID-19
pandemic. Infected people shed SARS-CoV-2 in their feces, therefore, virus levels in
wastewater reflect the trends in infection numbers in the population that contributes to the
wastewater. Wastewater surveillance offers information about the spread of SARS-CoV-2
independent of testing strategies and individual choices and therefore better reflects population
health than individual testing. In this study we compared five RNA extraction kits and reagents
and different enrichment methods for wastewater surveillance of SARS-CoV-2 RNA and
determined that the solids fraction of wastewater was most suitable for RNA extraction using
the AllPrep PowerViral DNA/RNA Kit by Qiagen. We also examined the impact of storage on
wastewater samples and determined that while short-term storage does not affect the samples
significantly, longer storage changes the measured viral levels. We then used our protocol to
establish a wastewater surveillance for SARS-CoV-2 viral levels in Regina and nine other
locations in Southern Saskatchewan, Canada. We observed that the three waves of COVID-19
during the study time were caused by the variants Alpha, Delta and Omicron and that the SARSCoV-
2 RNA levels in wastewater were a good reflection of the reported active COVID-19 case
numbers.