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Bayesian probabilistic projections of future climate over Canada based on the RCM ensemble
No full textA Thesis Submitted to the Faculty of Graduate Studies and Research In Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy in Environmental Systems Engineering, University of Regina. xxiii, 200 p.In this research, a series of approaches are proposed to address the challenges in
generating robust probabilistic projections of climatic variables (e.g., temperature,
precipitation, and Intensity-Duration-Frequency curves) and analyzing the associated
uncertainties based on the Regional Climate Model (RCM) ensemble. The proposed
approaches have been applied to Canada for demonstrating their effectiveness.
Specifically, a new discriminant-Bayesian Model Averaging (BMA) ensemble
climate modeling (DBEC) approach is developed to help address the neglected
spatiotemporal variations of model biases. Through the proposed method, the BMA
weights are used as prior distributions to drive the Bayesian discriminant analysis in order
to generate refined weights for individual ensemble models according to their spatiallyand
temporally-clustered performance. The results suggest that the DBEC approach can
improve both the accuracy and reliability of ensemble projections to some extent,
especially in winter and Arctic regions. The probabilistic projections of temperature for
three future periods under two emission scenarios are then obtained through the proposed
DBEC model. The results indicate that comparatively larger temperature increases can be
observed in Arctic regions. In addition, the magnitude of uncertainties is found to be
negatively correlated to the elevation.
Then a new multi-dimensional discriminant-BMA ensemble approach (MDBE) is
developed to quantitively characterize the relationships between the modeling
performances and climatic conditions. Through the comparative assessments of the
proposed approach against three other ensemble methods, its effectiveness in generating
the probabilistic projections of annual and seasonal precipitation over Canada has been
illustrated. In detail, the R2 and percentage coverage will increase up to 0.15 (from 0.52 to
0.67) and 20% (from 60% ~ 80%), respectively. The generated projections suggest that
significant precipitation increases are observed in future periods, especially in the Arctic
regions. The warming climate could be the primary reason for such increases. Moreover,
the intensified atmospheric radiative cooling is also a possible explanation for the winter
precipitation increase.
Finally, a new CDF-distance-based method is proposed to generate ensemble
projections of IDF curves over Canada. Compared with the traditional ensemble methods,
the proposed CDF-distance-based ensemble approach depends less on the simulated
accuracy of annual maximum precipitation time series. Consequently, it can improve both
the accuracy and reliability of the probabilistic projections in IDF curves. The proposed
method has been applied to Canada for assessing the future changes of the IDF curves.
The results suggest that the upward shifts of the IDF curves under all return periods are
observed under changing climate conditions. Moreover, the percentage changes of
precipitation intensities increase with return periods.Studentye
Formula Feeding Stigma and Perceived Controllability: How different rationales for formula feeding impact judgements
No full textDeep transfer learning-based DDoS attack detection in 5G and beyond networks
No full textA Thesis Submitted to the Faculty of Graduate Studies and Research In Partial Fulfillment of the Requirements for the Degree of Master of Science in Computer Science, University of Regina. xvi, 74 p.Network slicing is a crucial technology for enabling 5G and beyond mobile networks
which support a wide range of new services such as Enhanced Mobile Broadband
(eMBB), Ultra-Reliable and Low Latency Communications (URLLC), and Massive
Machine-Type Communications (mMTC) on the same physical infrastructure. However,
this technology also makes networks more vulnerable to cyber threats, especially
Distributed Denial-of-Service (DDoS) attacks. These kinds of attacks can degrade service
quality by overwhelming essential network functions necessary for the seamless
operation of network slices. To address this issue, an Intrusion Detection System
(IDS) is needed to protect against various DDoS attacks. A promising solution is the
use of Deep Learning (DL) models to detect potential DDoS attacks, a method already
proving effective in the field. However, DL models require large amounts of labeled
data for effective training, which are often scarce in operational networks. To address
this, Transfer Learning (TL) techniques can be used by transferring knowledge from
previously trained models to a target domain with limited labeled data.
In this thesis, Bidirectional Long Short-Term Memory (BiLSTM), Convolutional
Neural Network (CNN), Residual Network (ResNet), and Inception are used as base
models for Deep Transfer Learning (DTL) methods that look into how they can improve
DDoS attack detection in 5G networks. A comprehensive dataset generated in
our 5G network slicing testbed, which contains both benign and various DDoS attack
traffic, serves as the source dataset for DTL. After learning features, patterns, and
representations from the source dataset, the base models are fine-tuned using different
TL processes on a target DDoS attack dataset. The 5G-NIDD (5G Network Intrusion
Detection Dataset), which has limited annotated traffic from several DDoS attacks
generated in a real 5G network, is chosen as the target dataset. The results indicate
that the proposed DTL models improve the detection of various DDoS attacks in the
5G-NIDD dataset compared to models without TL. Specifically, the BiLSTM and Inception
models are identified as the top performers. BiLSTM shows an improvement
of 13.90%, 21.48%, and 12.22% in terms of accuracy, recall, and F1-score, respectively,
while Inception demonstrates a 10.09% increase in precision compared to models not
using TL.Studentye
Incorporating game theory with soft sets for better decision making
No full textA Thesis Submitted to the Faculty of Graduate Studies and Research In Partial Fulfillment of the Requirements for the Degree of Master of Science in Computer Science, University of Regina. vii, 71 p.Solving uncertainty is a challenge for decision-making. Soft set theory aims to aid
complex decision-making when multiple uncertainty variables are involved. To solve
classification problems with the existence of uncertainty, we adopted three-way classification
instead of binary classification. It introduces a boundary region to handle
scenarios in which a number of objects cannot be categorized as either positive or
negative with a high degree of certainty. The three-way classification problem involves
multiple experts. Each expert may produce a different three-way classification
outcome based on their available information and expertise. We introduced a gametheoretic
soft set model to address the fusion of partial information which is available
to certain experts and resolve conflicts among experts when determining the final
three-way classification. It uses a soft set to represent experts and formulates a game
among parameters of the soft set. The model is utilized to establish measurement
thresholds for parameters. The experiment shows the model is capable of striking a
balance among different parameters, resulting in a decrease in misclassification error
in an environment involving uncertainty. Furthermore, the extent of the decrease can
be fine-tuned by adjusting the ratio between the cost for misclassification error and
the cost for undecided error. Based on the user’s specified target misclassification
error and undecided error, our model can help determine an appropriate ratio.Studentye
From gee to haw (and everything in between): Deconstructing the transspecies pidgin of mushing in northern Saskatchewan
No full textA Thesis Submitted to the Faculty of Graduate Studies and Research In Partial Fulfillment of the Requirements for the Degree of Master of Arts in Anthropology , University of Regina. viii, 97 p.This multispecies ethnography deconstructs the Gee Haw transspecies pidgin of settler origin
mushing in Northern Saskatchewan, Canada. Through a focus on multisensory methodological
inquiry, I describe interspecies communication and human perceptions around nonhuman
knowledge in the context of sled dog racing. This pidgin is a product of biconstructivism which
includes motherese (verbal) words rooted in the English language, vocalisation, short phrases for
reinforcement, the use of material devices, and training methodologies to shape a multisensory
experience of interspecies collaboration. Dogs are considered nonhuman athletes in this hybrid
community. They are bred, cared and trained for their “drive”, speed, endurance and the ability to
understand commands. The sled dog exchange is explored through the lens of team sports and
perceptions around nonhuman “occupation” are explained. This study aims to contribute towards
an academic space given to more than human communication by detailing its findings from Gee
(right) to Haw (left) and everything in between.
Keywords: mushing, pidgin, dog-human communication, nonhuman athletes, haptic socialityStudentye
Nanocellulose-based materials for sustainable soil remediation and water purification
No full textA Thesis Submitted to the Faculty of Graduate Studies and Research In Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy in Environmental Systems Engineering, University of Regina. xxv, 275 p.Soil and water pollution are intricately linked environmental issues that have gained
significant global attention due to their adverse effects on ecosystems, public health, and overall
sustainability. In this dissertation, the pressing need for sustainable pollutant treatment using ecofriendly
and biodegradable nanocellulose (CNC) biopolymers is addressed. This research focuses
on the development of CNC-based materials, characterization of their adsorption behaviors,
evaluation of CNC-mediated algal toxicity, and exploration of the application of the these
materials in sustainable soil remediation and water purification.
In the first part, the utilization of CNC nanofluid as an eco-friendly agent for the remediation
of phenanthrene (PHE) contaminated soil is proposed. This marks the first exploration of CNC
nanofluid’s effectiveness in mobilizing PHE in soil, with a focus on the influence of
environmental factors. The findings demonstrate the critical role of temperature and ionic
strength in PHE removal. This study also reveals the interactions between CNC and soil
components, elucidating the primary PHE removal mechanism. Additionally, our research
highlights the detoxification effect of CNC nanofluid on PHE-contaminated soil, providing a
promising alternative for site remediation.
In the second part, inspired by the hierarchical fibrous structure and antibacterial properties of
natural silkworm cocoons, a guanidine-functionalized sericin/nanocellulose aerogel (GSNA) is
designed for application in the rapid removal of both bacteria and heavy metals from water. The
grafted polyhexamethylene biguanide (PHMB) endows the biomimetic aerogel with exceptional
bactericidal activity. The incorporated sericin protein brings abundant surface functional groups
for heavy metal complexation. Moreover, this study provides in-depth insights into the bonding
mechanism between metal ions and GSNA through density functional theory (DFT)-assisted X-
ray absorption near edge structure (XANES) analysis, representing a pioneering effort in
elucidating the adsorption mechanism of heavy metals within nanocellulose-based aerogels.
In the third part, a recyclable sericin/nanocellulose composite aerogel (SNCA) is introduced
for efficient tetrabromobisphenol A (TBBPA) removal from water. The developed SNCA
exhibits exceptional compressibility, hydrophilicity, and adsorption capacity. In addition, the
SNCA can be easily recycled through a simple compression method, demonstrating remarkable
reusability even after 10 regeneration cycles. Furthermore, toxicity evaluations reveal that SNCA
effectively mitigates the adverse effects of TBBPA on freshwater algae, emphasizing its
environmental friendliness. DFT calculations provide insights into the TBBPA adsorption
mechanism, indicating the involvement of hydrogen bonding and electron donor-acceptor
interactions.
In the fourth part, the investigation reveals that the presence of CNC significantly reduce ZnO
NP aggregation, enhancing bioavailability and toxicity to freshwater algae. The interaction of
ZnO NPs with CNCs leads to envelopment of algal cells and induces oxidative stress, affecting
membrane lipids and antioxidant enzyme activity. The introduction of CNCs enhances
intracellular transportation of Zn ions, influencing substance flow between algae cells and the
environment. This study advances our understanding of the combined effects of multiple
nanomaterials on aquatic organisms, allowing for the identification of composite risks.
In summary, this research explores novel and sustainable approaches for pollutant treatment
and environmental remediation, utilizing biodegradable nanocellulose materials. These efforts
contribute to reducing environmental impact and promoting eco-friendly solutions for soil and
water purification.Studentye
Adapting Cognitive Remediation Group Therapy Online: Focus Groups with People Aging with HIV
No full text Cognitive health is a significant concern for people aging with HIV/AIDS. Psychosocial group therapies may help people aging with HIV who experience cognitive challenges cope with their symptoms. The COVID-19 pandemic revealed in-person group therapies need adaptation for technology-mediated delivery. Peer-led focus groups discussed adapting cognitive remediation group therapy (CRGT) as an online intervention. CRGT combines mindfulness-based stress reduction and brain training activities. Purposive sampling recruited people aging with HIV (40+) who self-identified cognitive concerns and resided in one of two Canadian provinces. Thematic content analysis was employed on transcripts by seven independent coders. Ten, 2-hour focus groups were conducted between August and November 2022. Participants (n=45) responded favorably to CRGT's modalities. Alongside support for its continued implementation in-person, participants requested online synchronous and online asynchronous formats. Preferred intervention facilitators were peers and mental health professionals. We also discuss how to adapt psychosocial HIV therapies for technology-mediated delivery. This work was supported by the Canadian Institutes of Health Research (CIHR), HIV/AIDS
and STBBI Research Initiative (grant numbers 189187 and 478015) and
by the CIHR Canadian HIV Trials Network (grant number PT029)
Exploring factors that influence trust in non-standard stem cell therapies among patients with musculoskeletal conditions
No full textA Thesis Submitted to the Faculty of Graduate Studies and Research In Partial Fulfillment of the Requirements for the Degree of Master of Public Policy, University of Regina. x, 86 p.Although stem cell interventions (SCIs) may offer some therapeutic potential, the
development of regulatory frameworks for their safe clinical application remains a significant
challenge. As the regulation of these innovative therapies is still being developed, it is crucial
to examine the factors that shape patients’ trust in these interventions that lack clear
oversight. The purpose of this study is to explore the factors that influence the trust in nonstandard
SCIs among patients with musculoskeletal disorders as well as their understanding
of the role regulatory bodies play in ensuring safe and effective treatments. This
understanding will be relevant to policy development and regulatory reform for innovative
regenerative medicine therapies, potentially addressing the role that professional regulation
plays in providing oversight of this developing field.
This study employed a qualitative approach, using constructivist grounded theory.
The data were obtained through in-depth, semi-structured one-on-one interviews with eight
participants lasting from 45 to 75 minutes. The interview transcripts were analyzed initially
with line-by-line coding, then focused coding. The codes were later collapsed and organized
into categories, which guided theory construction.
The findings unveiled a range of factors involving the patients, their knowledge of the
intervention, and their practitioners that influence their trust in non-standard SCIs. The results
also suggest that health practitioners play a central role in guiding participants' consideration
of non-standard SCIs. This role also extends beyond medical doctors and includes allied
healthcare professionals, as patients with musculoskeletal conditions often seek their services
to manage their symptoms. Lastly, the results indicate a strong and implicit trust that patients
place in regulatory bodies; suggesting that patients hold expectations of these bodies without
a full understanding of how they meet them.Studentye
Microfluidic investigation of cyclic solvent injection: from reservoir-on-the-chip to large scale
No full textA Thesis Submitted to the Faculty of Graduate Studies and Research In Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy in Petroleum Systems Engineering, University of Regina. xxi, 210 p.Cyclic Solvent Injection (CSI) is an effective Enhanced Oil Recovery (EOR) technique with several economic and environmental benefits. Understanding the key mechanisms and developing reliable scaling criteria are required to successfully implement CSI commercially. In this study, CSI was assessed from both microscopic and macroscopic perspectives using experimental and numerical methods. Regarding the experimental studies, the characteristics of the heavy oil/solvent systems used were evaluated through a detailed PVT analysis that included Constant Composition Expansion (CCE) and Differential Liberation (DL) tests. Moreover, this study examined the microscopic behaviour of foamy oil flow on microfluidic platforms, examining the mechanisms involved in bubble evolution.
Based on the visualization studies conducted on the microfluidic systems, it was found that solvent type, pressure depletion rate, and reservoir characteristics had a significant influence on the extension of foamy oil flow. Accordingly, solvents containing a higher proportion of CO2 exhibited superior performance, primarily due to their ability to lower viscosity, enhance swelling, and deliver more gas molecules. Additionally, a higher pressure-depletion rate increases the driving force for bubble nucleation while limiting the time available for bubble coalescence. Moreover, lower reservoir porosity interferes with bubble movement and slows down coalescence, prolonging the foamy oil flow. In addition, Sandpack experiments showed that the Cumulative Gas Oil Ratio (CGOR) is a key indicator of foamy oil flow. Even below bubble point pressure, CGOR remains nearly constant as exsolved gas disperses rather than forming free gas immediately. As part of the simulation study, a numerical model was developed using the CMG software package that captures the non-equilibrium behaviour of the foamy oil flow by utilizing two pseudo-chemical reactions including bubble generation and bubble coalescence. Additionally, to minimize the discrepancy between simulation predictions and experimental results, CMG CMOST was used to tune the oil and gas relative permeabilities as well as reaction rate frequency factors.
CSI has been also formulated comprehensively by integrating material balance, mass transfer, and pseudo-chemical reaction equations to derive key dimensionless scaling terms based on the Buckingham π Theorem. 11 dimensionless terms have been identified, which encompass a wide range of phenomena, including foamy oil mobility and its intricate dynamics, as well as solvent exsolution processes. As a result, a comprehensive procedure has been developed for scaling up laboratory results to larger systems. In addition, to account for pressure propagation delay in larger reservoirs, an effective workflow was established to systematically modify the permeability in lab settings in such a manner that its results can be translatable into larger models. Based on analysis of two synthetic reservoirs, a reasonable match in terms of recovery factor, cumulative gas production per unit pore volume, and CGOR versus dimensionless time between the synthetic reservoirs and the Sandpack models was demonstrated, which highlights the robustness and effectiveness of the proposed scaling method. The proposed scaling workflow offers a foundation for future research on scaling methodologies in solvent-based heavy oil recovery processes. Moreover, it can be used to optimize recovery strategies and reservoir management by enabling more accurate predictions of CSI performance at larger scales.Studentye
Assessing construction and demolition waste generation rates using satellite imagery
No full textA Thesis Submitted to the Faculty of Graduate Studies and Research In Partial Fulfillment of the Requirements for the Degree of Master of Applied Science in Environmental Systems Engineering, University of Regina. ix, 110 p.Municipal solid waste management has seen a surge in the use of satellite imagery
in decision-making processes, yet its application to analyze quantitative variations in
construction and demolition (C&D) waste remains underexplored. Especially the COVID-
19 pandemic disrupted conventional municipal solid waste (MSW) management practices
and affected waste generation rates. While MSW streams have been extensively studied
and reported, the impact on construction and demolition (C&D) waste remains overlooked.
As such, the first part of the study develops an innovative analytical framework utilizing
satellite imagery to quantify C&D waste disposal rates during COVID-19 restrictions in a
mid-sized Canadian city. Supervised classification of Landsat-8 images is conducted to
derive the settlement area over a period of 8.8 years (2014-2022). The relationship between
C&D disposal rates and settlement area is evaluated using regression analysis. Results
reveal a 73.4% reduction in mean weekly C&D disposal in 2020 compared to pre-pandemic
years, reflecting diminished construction activity. The settlement area exhibits a strong
positive correlation (R2=0.812) with per capita C&D disposal rate, providing spatial
evidence of urbanization patterns affecting C&D waste generation. Among socioeconomic
factors examined, the value of building permits issued most influences C&D quantities
(R2=0.934). The satellite imagery-based approach allows indirect estimation of disrupted
C&D waste streams when on-site auditing is restricted during pandemics. The framework
offers municipal authorities spatial decision support to formulate data-driven C&D waste
management policies that are resilient to future public health emergencies.
The second part of the study employs satellite imagery and multivariate analysis to
comprehensively assess and predict C&D waste generation in four diverse urban
jurisdictions of Canada (Regina) and the USA (Seattle, Buffalo, and Philadelphia). In
Seattle, the annual mean C&D waste amount per capita is 0.624 tonnes, while in Regina,
Buffalo, and Philadelphia, it is 0.224, 0.330, and 0.014 respectively. Factors such as
settlement area expansion, economic activities, and population growth significantly
influence C&D waste rates. Stepwise multivariate regression models tailored to different
city types, such as moderately populated (Group 1) and highly populated (Group 2),
showcase acceptable predictive capabilities. For moderately populated cities, settlement
area, average humidity, and GDP are identified as key predictors, while for highly
populated cities, settlement area, unemployment rate, and building permit value prove
effective indicators. These models, characterized by R² values from 0.70 to 0.94, provide
tailored insights for distinct demographic conditions, aiding waste management planning.
This research underscores the importance of satellite imagery and multivariate analysis in
understanding C&D waste dynamics and empowers policymakers and waste management
agencies with evidence-based strategies for effective waste management in urban centers.Studentye