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Talk With Everything: Interspecies Communication from the Circumpolar North
No full textThis book brings together a group of anthropologists, offering insights from their most recent work with Indigenous and Settler communities on animal–human relationships. Covering experiences from Canada, Russia, Mongolia, and the USA, the book investigates how humans and animals express their intentions to each other, how interspecies communication can help detect the presence of intangible entities and interpret their attitude, how different ways of reading animals can conflict (and how this conflict can be resolved), as well as how desires and preferences can be understood across the world of sentient beings, whether at home or in the wild
Adaptive systems for DDoS attacks detection and mitigation in IoT networks
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 Electronic Systems Engineering, University of Regina. xxiii, 374 p.The rapid growth of IoT devices has revolutionized industries while exposing
IoT networks to cybersecurity threats, particularly DDoS attacks, which compromise
network stability. Traditional detection methods struggle to address the constraints
of resource-limited environments, scalability, and the need for lightweight,
optimized, and reliable systems. This thesis addresses these challenges through five
objectives aimed at adaptive DDoS detection and mitigation systems for IoT networks,
balancing accuracy, resource efficiency, and adaptability. The first objective
focuses on developing a Flow and Unified Information-based DDoS detection system
(FLUID) for small-scale IoT networks, enabling DDoS detection with minimal computational
overhead. The FLUID system uses flow metrics and unified information
measures, to detects both high and low-volume attacks while optimizing resource
use. The second objective introduces a system with novel hybrid feature selection to
enhance detection accuracy in medium-scale IoT networks. By combining Genetic
Algorithm and t-test for DDoS Attack Detection (GADAD), this system improves
feature selection efficiency and supporting binary and multiclass classification. For
large-scale networks, the third objective is the design of a Deep Ensemble Learning
with Pruning (DEEPShield) system that integrates CNN and LSTM architectures,
optimized through post-training pruning and a novel preprocessing method.
This system achieves high detection accuracy with low resource demand, suitable for
resource-constrained IoT environments. The fourth objective focuses on optimizing
deep learning-based detection systems to enhance resource efficiency and explainability
using the OMEGA, ADEPT, and SHIELD systems. The Optimized Ensemble
Learning with Pruning (OMEGA) and Interactive and Explainable Optimized Learning
(ADEPT) systems apply techniques like genetic algorithms and differential evolution
for resource efficiency. The SHAP-Based Explanation and Lightweight DDoS
Attack Detection (SHIELD) system uses SHapley Additive exPlanations (SHAP)
for interpretability of individual predictions. The final objective addresses adaptive
mitigation through a Game-Theoretic DDoS Defense Strategy Model (GT-DDSM)
that dynamically adjusts defense strategies based on attack intensity. These systems
are evaluated on metrics such as accuracy, precision, recall, F1-score, and scalability,
while optimization efficiency is assessed by preprocessing time, inference speed,
memory usage, and model size. Explainability is assessed through SHAP and priority
assessment values, while mitigation effectiveness is measured by gradients, cumulative
payoff, mitigation time, resource utilization, and network QoS parameters.Studentye
Feeling detached: The central role of detachment in a network study of posttraumatic stress symptoms in Public Safety Personnel
No full textBackground: Due to the nature of their work, Public Safety Personnel (PSP; e.g., firefighters, paramedics, police
officers) are frequently exposed to potentially psychological traumatic events (PPTE) and are at increased risk of
developing posttraumatic stress symptoms (PTSS) compared to the general population. To date, there are a
limited number of published studies that have used the statistical tools of network analysis to examine PTSS in
PSP, typically relying on small, homogenous samples.
Basic procedures: The current study used a large (n = 5,319) and diverse sample of PSP to estimate a network of
PTSS and exploratory graph analysis to assess alternative structures of symptom clustering, compared to tradi-
tional latent models.
Main findings: The results of the analyses estimated two symptom clusters which differed from most latent models
of PTSS. Re-experiencing and avoidance symptoms clustered together, instead of in two clusters. Similarly, hy-
perarousal symptoms (hypervigilance, sleep disturbance, startle reflex, concentration difficulties) clustered in a
single community instead of two or three clusters in many latent models of PTSS. The symptom of detachment
played the most central role in the network and acted as a bridge symptom between numerous clusters of
symptoms. The least central symptom was amnesia, which also had the most inconsistent pattern of clustering
and bridging. Other bridge symptoms included negative emotions, difficulty concentrating, and reckless
behaviour.
Principal conclusions: The symptom of detachment played a pervasive role in centrality and bridging in a network
of PTSS in PSP. Future research is necessary to identify whether central PTSS differ across populations based on
their PPTE type (e.g., combat, assault, rape) or typical environmental factors (e.g., group cohesion in PSP and
military)
Archer Library Award 2025 - Reflective Essay
No full textDuring the 2024 Fall Semester I was enrolled in MUHI 202 – Music History 202 - in which the final project was a research paper and bibliography assignment based on a composer and one of the compositions. This research paper had multiple research heavy assignments that came before it. This assignment, and all the assignments before it, were designed to give the first year students in this class – which was most of us- a chance to learn the ins and outs of the Archer Library database
Why aquatic deoxygenation belongs in the planetary boundary framework
No full textThis work was supported by the Chancellor’s Postdoctoral Scholar Program at UC Santa Cruz with in kind support from the Kroeker Lab (EMF), a Chancellor’s Research Fellowship at the University of Technology Sydney (AKP), the Natural Sciences and Engineering Research Council of Canada (PRL), and a Society of Science Postdoctoral Fellowship from the University of Notre Dame (SFJ)
Manufactured memory
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 Fine Arts in Visual Arts, University of Regina. viii, 75 p.This paper is in support of the exhibition “Manufactured Memory” my Master of Fine
Arts graduating exhibition that was held at the Fifth Parallel Gallery from November 6th -
November 19th, 2024. This paper, and the exhibition explore my use of the conversational
Artificial Intelligence system ChatGPT to recreate a childhood memory that serves as a
framework for navigating themes of loneliness, longing, authenticity and failure.
I begin by describing the works in the exhibition and how they were installed. I then go
on to describe the development of the exhibition and the decisions that led to its final form. In
the third section I explain my artistic methods, techniques, and why I chose them. Finally, I
address two contemporary artists and a fictional novel that influenced the conceptualization of
this exhibition. In addition to this, there is a glossary of terms following the conclusion.Studentye
Secure and scalable blockchain mechanisms for IoT applications
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 Electronic Systems Engineering, University of Regina. xix, 270 p.Integrating blockchain with IoT ensures secure, transparent data exchange through
immutability and consensus mechanisms, preventing data tampering. However, the
increasing number of IoT devices raises risks like unauthorized access and network
attacks. Blockchain scalability issues also affect throughput and latency, challenging
real-time IoT applications. This thesis addresses these challenges through four contributions
that aim to improve the security, scalability, and efficiency of blockchainbased
IoT networks, balancing security with performance needs. Our first contribution
is to develop an end-to-end security mechanism for IoT networks, called the
trust-based ABAC mechanism for IoT networks (TABI). TABI integrates edge computing
and blockchain technology to mitigate risks from malicious devices and offload
computational tasks to edge layers. It operates on Hyperledger Fabric (HLF), a
permissioned blockchain that enhances throughput and latency through its executeorder-
validate architecture. Our second objective is to provide scalability within
blockchain-based IoT networks using a sidechain-based trust and access control system,
named sidechain-based trust and access control mechanism for IoT networks
(SATI). By distributing trust evaluation and access control operations across a separate
blockchain or sidechain, SATI improves the scalability of IoT networks. We
implement a cross-chain transfer mechanism to ensure communication between the
sidechain and the mainchain, thus overcoming a fundamental limitation of traditional
blockchain architectures. Our third contribution is to improve the security of the IoT
network by introducing a Zero-Knowledge Proof-based Mutual Authentication (ZPMA)
mechanism, a privacy-preserving mutual authentication mechanism. Utilizing
Zero-Knowledge Proofs (ZKP) based on the quadratic residue technique, Z-PMA ensures
secure and private mutual authentication between edge devices and IoT devices.
We also implement an incentive mechanism to select additional authenticators from
the base station layer to reduce authentication latency and support the demands of
low-latency IoT networks. Our fourth contribution is to detect and resolve conflicting
transactions in HLF-based IoT networks at an early stage, known as the early-stage
conflict transaction resolution (ECR) mechanism. ECR identifies and resolves conflicting
transactions at an early stage using a local cache at the endorsement phase
of the HLF transaction processing. Additionally, ECR uses dependency model and
an efficient reordering process to distribute transactions in a way that minimizes
conflicts. This mechanism enhances the performance of HLF-based IoT networks by
reducing the impact of conflicting transactions, ultimately improving throughput and
latency.Studentye
PeerOnCall: Evaluating Implementation of App-Based Peer Support in Canadian Public Safety Organizations
No full textPublic safety personnel (PSP), including correctional workers, firefighters, paramedics, police, and public safety communicators, are at increased risk for posttraumatic stress injury, yet face barriers in receiving timely support. Mobile health (mHealth) applications (apps) offer promising avenues for confidential, on-demand access to relevant information and support. The purpose of this study was to assess implementation of PeerOnCall, a new mHealth platform designed by and for PSP (the platform includes two parallel apps: one for frontline workers and one for peer support providers). A multi-site mixed methods implementation trial was conducted over 3−6 months in 42 public safety organizations across Canada. App usage trends were tracked through software analytics, and facilitators and barriers to app use were explored via interviews with organizational champions. Over 11,300 employees across 42 organizations were invited to use the PeerOnCall app over the trial period, with approximately 1759 PSP (15% of total) downloading the app. Variation within and across sectors was evident in app downloads and feature use. Approaches to communication (mode, timing, and messenger), and organizational culture related to mental health and help outreach affected uptake levels. PeerOnCall is a promising tool to facilitate access to peer support; however, culturally relevant strategies are needed to overcome barriers and integrate this tool into workplace practicesThis research was funded by Movember (P-000231), and the Public Health Agency of Canada (2122-HQ-000406). The APC was funded by the Canadian Institutes of Health Research (CIHR, RCP-179573)
Experimental characterization and machine learning optimization of polymer nanocomposite membranes for carbon capture systems
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. xx, 288 p.The study aimed to characterize the CO2 capture capabilities of Polyacrylonitrile (PAN) nanocomposite membranes by reinforcing them with multi-walled carbon nanotubes (MWCNT) and silica (SiO2). These membranes were made using the electrospinning manufacturing method. The nanoparticles were functionalized using Gum Arabic (GA) to improve nanoparticle distribution, which further improved the capture efficiency. The morphological techniques were used to examine the nanoparticle structures after functionalization to optimize the functionalization parameters. Experimental results showed that increasing nanoparticle concentrations enhanced CO2 permeability while maintaining stable N2 permeability, resulting in favourable CO2/N2 selectivity ratios. The 4 wt. % MWCNTs nanocomposite membrane exhibited the best CO2/N2 separation with a CO2 permeability of 289.4 Barrer and a selectivity of 6.3, while the 7 wt.% SiO2 nanocomposite membrane achieved a CO2 permeability of 325 Barrer and a selectivity of 7. These results indicated significant CO2 permeability and selectivity improvements compared to pure PAN membrane. The Maxwell mathematical model was used for validation, and the experimental results exceeded the predicted values, possibly due to well-dispersed nanoparticles and functional groups.
Based on the CO2 capturing results from the previous experiments, a second experiment study focused on enhancing the CO2 capture capabilities of PAN membranes by modifying them with polyethyleneimine (PEI), a polymer with high CO2 absorption capacity. PAN was modified with three weight fractions of PEI (25%, 40%, and 50%) and then reinforced with various weight fractions of MWCNT, SiO2, and alumina (Al2O3) nanoparticles. The reinforced PAN-PEI nanocomposite membranes were produced using an electrospinning technique. The morphological characterization techniques confirmed that the PEI has effectively modified PAN polymer, which has improved the distribution of nanoparticles within the nanocomposite membranes. Gas permeation tests revealed that the 40 wt.% PEI-modified membrane achieved the best CO2/N2 separation, with a CO2 permeability of 509.4 Barrer and selectivity of 7.4. The PAN with 40% PEI was then reinforced with 1, 4, 7, and 10 wt. % nanoparticles and the highest performance was observed with 7 wt.% Al2O3, resulting in a CO2 permeability of 849 Barrer and selectivity of 9.6. The results were validated using mathematical models (Resistant Model Approach and Effective Medium Approach), confirming the effectiveness of nanoparticle infusion for CO2 separation.
Finally, this research developed and applied three machine learning (ML) techniques, Deep Neural Networks (DNN), Random Forest (RF), and XGBoost models, to analyze the CO2 permeability and CO2/N2 selectivity of nanocomposite membranes. The datasets for CO2/N2 separation were sourced from our experimental results and the published experimental data available in the literature. Key performance metrics such as polymer type, nanofiller type, size, loading amount, membrane surface area and thickness, temperature, and feed pressure were analyzed. Feature importance plots provided insights into the most influential parameters for the material design. The study involved hyperparameter tuning of the DNN, RF, and XGBoost models to achieve optimal performance. Each model was tested using literature data and combined experimental and literature data to validate the models and assess the impact of incorporating experimental data. Performance metrics were evaluated to establish the research's credibility and generalizability. The XGBoost optimized ML model achieved the best prediction performance, with R2 values of 0.93 for CO2 permeability and 0.83 for CO2/N2 selectivity, highlighting the effectiveness of using ML for optimizing nanocomposite membranes.Studentye
Archer Library Annual Report 2024-25
No full textOne of the significant events celebrated in this year's Annual Report is the (re)-gifting by Brad McNaughton of the University's leather crest that proudly hung in Darke Hall in the early years of Regina College. As you will see, the crest has had journeys known and unknown that took it far afield before coming home to the University Archives.
In many ways, the crest represents all of us. No matter how we became part of the University of Regina family - whether we grew up as part of its extended community, came into it as students, or committed to expanding knowledge, learning, and research as new faculty and staff members - we all have stories about how the U of R has woven its way through our lives.
But no matter how far we travel in our lives and careers, there is always a need to return home - to celebrate our successes, to share our memories, to inspire a love of learning in a new generation, and just to relax in a (mostly) familiar place.
The Dr. John Archer Library and Archives encapsulates all this. In these pages you will see many successes, more than a few memories, a definite love of learning, and a welcoming place that is always looking towards something new and exciting.
Thank you for being part of our family. We could not have built this home without you