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Now showing 1 - 10 of 19

Recent Submissions

Access status: Open Access ,
Sexual well-being among individuals undergoing fertility treatment: A review of recent literature
(2024) Péloquin, K.; Beauvilliers, L.; Benoît, Z.; Brassard, A.; Rosen, N. O.
Access status: Open Access ,
EXPLORING REAL-TIME MALICIOUS BEHAVIOUR DETECTION IN VANETS
(2026-02-20) BAHARLOUEI, HAMIDEH; Not Applicable; Doctor of Philosophy; Faculty of Computer Science; Not Applicable; Dr. Sudhakar Gant; Not Applicable; Dr. Riyad Alshammari; Dr. Srinivas Sampalli; Dr. Yujie Tang; Dr. Nur Zincir-Heywood; Dr. Tokunbo Makanju
Vehicular Ad Hoc Networks (VANETs) and Unmanned Aerial Vehicle (UAV) net- works are increasingly used in intelligent transportation systems and autonomous aerial missions. Due to their decentralized and wireless communication nature, these networks are vulnerable to cyberattacks such as Distributed Denial of Service (DDoS), spoofing, and message tampering. This thesis presents ADVENT (Attack/Anomaly Detection in VANETs), a distributed malicious behaviour detection framework de- signed to detect attack onset and identify malicious nodes in real time in both VANET and UAV environments, while preserving data privacy through federated learning. ADVENT integrates statistical analysis with supervised machine learning in a feder- ated learning architecture to support decentralized detection of malicious behaviours. The framework is evaluated under multiple attack scenarios and mobility models. A key methodological contribution is the design and integration of Adaptive Time Slicing (ATS) and Detection Threshold (DT) mechanisms within the malicious node detection (MND) component. These parameters can be tuned to accommodate dif- ferent network characteristics, including topology, node density, and communication dynamics. The ATS mechanism improves temporal detection resolution and mitigates the impact of transient misbehaviours by analyzing fine-grained behavioural snap- shots and aggregating evidence over time. This enhances the robustness of malicious node identification while reducing false positives and missed detections. ADVENT is evaluated using public and simulated datasets, including a custom VANET simu- lation (FourCities), the VeReMi-Extension dataset, a simulated UAV dataset, and a public cyber-physical UAV dataset. Its generalization capability is further examined using unseen attack types not included during training. Results show that ADVENT consistently achieves high F1 scores, low false positive rates, and timely attack onset detection across different network environments. By validating the framework in both ground-based and aerial vehicular networks, this thesis demonstrates the potential of federated learning–based approaches to provide scalable and privacy-aware security mechanisms for future intelligent transportation infrastructures.
Access status: Open Access ,
The impact of nasogastric tube gastric decompression on postoperative nausea and vomiting in orthognathic surgery
(2026-02-25) Curry, Katherine; Not Applicable; Master of Science; Faculty of Dentistry; Received; na; Not Applicable; Dr. Curtis Gregoire; Dr. Stephen Middleton; Dr. James Brady
The emetogenic effect of ingested blood is believed to be a major precipitating factor in the development of postoperative nausea and vomiting (PONV) following orthognathic surgery. This study aimed to determine whether perioperative nasogastric decompression with a nasogastric tube reduces the incidence of PONV. A randomized control trial of 133 patients was conducted, and participants were assigned to receive perioperative nasogastric decompression (n=64) or no decompression (n=69). Nausea and vomiting were assessed in the twenty-four-hour postoperative period and secondary outcomes evaluated patient and perioperative clinical factors associated with PONV. Nasogastric decompression did not significantly reduce PONV, although a lower incidence of symptoms was observed in the nasogastric decompression group. Opioid use was the only variable independently associated with increased PONV. These findings suggest that nasogastric decompression alone does not significantly reduce PONV following orthognathic surgery, but may be a useful intervention as part of a multimodal prevention strategy.
Access status: Open Access ,
Sex-specific need fulfillment in relationships and sexual and relationship wellbeing
(2024) McClung, E., Rosen, N. O., Muise, A., Kannathas, S., & Corsini-Munt, S.
Access status: Open Access ,
Image-Based Random Field Algorithm for Nonlinear Finite Element Analysis of Cracked Reinforced Concrete Members: Supplementary Material
(2026) Alhashmi, Abdalla Elhadi; Oudah, Fadi
Access status: Open Access ,
The Vehicle Scheduling Problem: Models, Complexity and Algorithms
(1988-03-28) Cyrus, James Pemberton; Doctor of Philosophy
The Vehicle Scheduling Problem with time windows (VSP) is concerned with finding a minimum-cost set of vehicle schedules which processes a set of jobs with specified earliest and latest start times, durations and trip times between jobs. This problem is well known to be very difficult to solve. A proof that the VSP decision problem is NP-complete is developed in order to gain a deeper understanding of the reasons for the problem's complexity. The ideas are extended to measuring the difficulty of integer instances of the problem. A graph-theoretic representation of the VSP is combined with ideas related to problem difficulty and this leads to theorems on reducing the difficulty of problem instances. The techniques are aimed at reducing the number and sizes of the time windows, therefore greatly reducing the size of the feasible region. The window reduction methods are shown to be both theoretically and empirically valid. The VSP is formulated as a constrained assignment problem, and the start times of jobs are replaced by time window variables. This new formulation leads to theorems on bounds for the VSP, and to a class of approximation algorithms with strong convergence properties. The algorithms prove to be effective in solving both easy and hard problems. The ideas are extended to produce exact algorithms for the VSP. Extensions of the VSP model to include earliness, waiting time and return trips are formulated. The optimal solutions to some special cases of these problems are developed. The approximation algorithms for the VSP are easily adapted to these extended models. An interactive system is developed to build and test new algorithms. This facility allows fundamental algorithms to be combined to produce new results. The user interfaces allow multiple solution views and manipulations to gain insight into the solution structures.