Lakehead University Knowledge Commons

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Bioretention for fish habitat protection: treatment performance and spatial prioritization in a cold climate
(2026) Muir, Brant; Stewart, Robert; Rennie, Michael; Drake, Jennifer; Kolka, Randall; Khan, Usman
Urban stormwater and meltwater mobilize pollutants and transport them into urban streams, where they pose ecotoxicological risks to aquatic life. Bioretention systems (a.k.a. bioretention cells or rain gardens) are a form of green stormwater infrastructure (GSI; a.k.a. low impact development practice or LID) that retain and filter runoff, reducing volumes and improving water quality before it enters receiving watercourses. This dissertation evaluates the potential for bioretention systems to support urban fish habitat protection by reducing runoff volumes and improving water quality before discharging into urban streams. It also examines whether event-scale stormwater and meltwater treatment improvements are detectable through short-term downstream water quality monitoring and identifies where stormwater interventions should be prioritized to support the protection of sensitive fish habitats. A perspective chapter outlines an integrated approach to urban stream restoration that combines physical habitat improvements with green stormwater infrastructure practices, such as bioretention systems, within urban land use planning and renewal. The chapter emphasizes post-restoration monitoring, incorporating water quality guidelines into GSI performance evaluations, strategically locating restoration and stormwater interventions where they are most likely to support ecological protection, and increasing stormwater management on private property through public engagement. Field investigations evaluated three bioretention systems discharging into trout-sensitive urban tributaries in Thunder Bay. Water quality was monitored at all three systems, while water quantity was monitored only at Bioretention Systems 1 and 2 because site constraints at Bioretention System 3 prevented reliable inflow and outflow discharge measurements. During rainfall events, Bioretention Systems 1 and 2 fully retained runoff during 43 and 70 of 87 monitored events, respectively. When effluent occurred, suspended solids concentrations decreased by 51-64% across the three bioretention systems and turbidity was reduced at two of the three systems. These reductions in runoff volumes, turbidity, and suspended solids suggest a reduced potential for particulate pollutant delivery and fine sediment inputs into fish-bearing streams. Additional analyses examined pollutant accumulation in the winter snowpack and bioretention performance during the spring freshet. Roadside snowbanks contained significantly higher concentrations of chloride, suspended solids, and dissolved organic carbon than open field and bioretention sites. During spring melt, peak and total meltwater volumes were reduced at Bioretention Systems 1 and 2, where hydraulic monitoring was feasible, while water quality was evaluated across all three systems. Across the three systems, pH, turbidity, suspended solids, and dissolved organic carbon concentrations decreased in meltwater before discharging to receiving waters. A rapid assessment framework integrating stormwater impairment data and habitat surveys was developed to identify priority locations for green stormwater infrastructure. Applied to a trout-sensitive tributary in Thunder Bay, Ontario, this framework provides municipalities with a practical tool to prioritize stormwater interventions in locations where they are most likely to support the protection of sensitive fish habitats. This dissertation makes several novel contributions to stormwater management and fish habitat protection in a cold climate. First, it provides a critical perspectives-based synthesis that identifies why urban stream restoration, stormwater management, and land use planning can fail to protect urban streams when implemented independently. Rather than simply arguing that these practices should be integrated, this chapter clarifies specific management disconnects between these practices. It is argued that stream habitat restoration focusses on improving physical fish habitat, but does not adequately address impacts from untreated stormwater runoff, that GSI may reduce runoff quantity and improve runoff quality at the site level, without producing detectable ecological recovery, and that land use planning may miss opportunities to reduce future pollutant loading, protect sensitive areas, or support GSI implementation on private land. The contribution of this chapter is a critical synthesis that reframes urban stream revitalization as an integrated planning challenge rather than a separate set of stream restoration, stormwater and land use practices. Stream habitat restoration projects and green stormwater infrastructure (GSI) aim to protect urban streams, but are often implemented independently. This chapter provides a critical perspectives-based synthesis that reframes urban stream revitalization as an integrated planning challenge. It highlights how isolated approaches can limit ecological recovery and identifies strategies to coordinate habitat restoration, GSI performance evaluation, winter snow monitoring, stormwater controls on private land, and riparian protection in cold-climate urban watersheds. Recommendations include post-restoration monitoring, aligning restoration with local degradation, strategic placement of GSI and habitat improvements, and incorporating land-use planning and zoning to protect sensitive areas. These recommendations provide the conceptual framework for the field-based and applied chapters that follow. Second, it provides empirical evidence from rainfall and snowmelt events demonstrating that bioretention systems can substantially reduce runoff volumes and particulate-associated pollutants before discharging into trout-bearing waters. Third, it demonstrates that treatment performance differs between particulate and dissolved contaminants, with high reductions in turbidity and suspended solids concentrations, but limited or inconsistent reductions in chloride and nutrients, emphasizing the need to reduce pollutant sources at the source through changes in land use practices to complement bioretention treatment performance. Fourth, it identifies roadside snowpack as an important seasonal reservoir of sediment, chloride, and organic carbon, highlighting the influence of winter road maintenance, vehicular activity and spring freshet processes on cold climate stormwater quality. Lastly, it develops a spatial prioritization framework that combines stormwater impairment identification with downstream fish habitat sensitivity analysis to guide green stormwater infrastructure placement where the ecological benefit to fish habitats is most likely.
High-performance model predictive control methods for multilevel inverter-fed medium-voltage drive systems
(2026) Le, Hoang; Dekka, Apparao
This dissertation presents comprehensive research on the modeling, control, and implementation of advanced multilevel inverter (MLI) topologies and model predictive control (MPC) strategies for medium-voltage (MV) drive systems. The primary objective is to achieve superior current tracking performance, reduced switching frequency, and minimized common-mode voltage (CMV), while maintaining low computational complexity. The research addresses critical limitations in existing MLI topologies and MPC methods such as high component count, increased cost and size, model inaccuracy, high computational burden through the development of novel converter configurations and control methodologies. A new five-level (5L) inverter topology is first proposed, featuring a reduced number of components and the elimination of multiple isolated DC-sources. The topology utilizes only flying capacitors (FCs) and switches, thereby reducing control complexity compared to existing 5L-MLI. A finite-control-set-MPC (FCS-MPC) method is also developed to control the proposed 5L-MLI, and the performance of the inverter is experimentally validated under various operating scenarios. Results demonstrate that the proposed inverter has superior harmonic performance and low switching power losses while operating at low switching frequency in comparison to the existing 5L-MLIs. Besides converter configurations, control methods play a pivotal role in system performance. Existing FCS-MPC are modeled based-on the forward Euler’s integration method due to its ease of implementation but suffer from significant prediction errors at larger sampling periods. To tackle this issue, a Heun integration-based-FCS-MPC approach is proposed for MLIs. The proposed method incorporates correction stage along with prediction stage to improve the prediction accuracy, resulting in a substantial reduction in current tracking error and switching activity. Experimental results confirm the effectiveness of the proposed approach through enhanced prediction accuracy while operating at a low switching frequency. To further tackle CMV and computational challenges, improved sequential MPC (SMPC) strategies are proposed. The proposed low-complexity SMPC eliminates the reliance on weighting factors and offline switching vector preselection to reduce the CMV. In addition, an enhanced sampled-data SMPC is proposed to improve the discrete-time model precision, significantly reducing current distortion and FC voltage ripple. Experimental validation on an MLI prototype demonstrates their excellent current regulation, lower CMV, and improved performance compared to existing SMPCs. Finally, an SMPC strategy with cost function-free current control and CMV mitigation is proposed based on the low-complexity SMPC framework. By directly determining the optimal voltage level from the reference AC currents, the proposed method removes the need for cost function optimization in the current control stage, while maintaining low CMV and reducing computational complexity. Experimental and simulation results demonstrate effective current regulation, low harmonic distortion, reduced FC voltage ripple, and satisfactory motor drive performance, confirming the practical suitability of the proposed SMPC for high-performance MLI-fed MV drive systems.
Large-scale news headline quality analysis: clickbait trends, binary classification, and AI-generated content
(2026) McCutcheon, Austin; Brogly, Chris; Tan, Xing; Yang, Xingwei (Nancy)
Online news can be characterized by massive volumes of news content spanning a spectrum from high-quality professional journalism to low-quality articles. This thesis presents four empirical studies that employ methods to analyze, classify, and evaluate quality-varying news headlines at scale. The first two studies apply Interrupted Time Series (ITS) analysis to examine associations between clickbait prevalence and major events. Analysis of 451 million headlines from worldwide news websites (2016-2023) revealed statistically significant associations for three of five events, each showed slight pre-event decreases followed by sustained post-event increases in clickbait levels. A complementary analysis of 7.4 million headlines from Canadian news websites (2017-2023) found similar patterns. The third study benchmarks twelve machine learning and deep learning models for binary classification of perceived news quality on a balanced dataset of 57.5 million headlines labeled according to website-level expert consensus ratings. Results demonstrated that a CPU-based Bagging Classifier achieved 88.1% accuracy with stability across cross-validation folds, while a fine-tuned DistilBERT model achieved the highest accuracy at 90.3% but required substantially greater computational resources. The fourth study evaluates fourteen accessible Small Language Models (SLMs) for their willingness to generate fake news headlines when explicitly prompted and tests whether the trained classifiers from study three generalize to synthetic content. Minimal resistance to generating false news headlines was found, with models refusing requests less than 1% of the time. Both classifiers showed substantially reduced performance on AI-generated headlines (54-63% for DistilBERT, 35-48% for Bagging), with systematic misclassification of AI-generated “high-quality” content as “low-quality,” suggesting that human-trained classifiers do not generalize effectively to current AI-generated text. This thesis contributes the application of ITS methodology to clickbait analysis at web scale, comprehensive benchmarking of model architectures for large-scale headline quality classification, and empirical evidence that quality classifiers trained on human-authored content exhibit reduced performance when applied to SLM-generated headlines.
On the study of fluid entry by solid objects and bubble column dynamics
(2026) Ebrahimi, Mohammadamin; Azimi, Amir; Liao, Baoqiang; Elshaer, Ahmed; Balachandar, Ram
This research addresses the fluid-entry behavior of falling rigid bodies—specifically cylindrical disks and spheres—impacting a quiescent ambient fluid. With direct relevance to natural and engineered systems. The study resolves how variations in fluid properties such as fluid density, viscosity, and object geometry such as diameter, thickness, and edge profile, and the solid–fluid density ratio shape the entry process. Such entry processes are including splash/crown formation, cavity growth and pinch-off, added-mass effects, penetration trajectories, and the mixing field that develops behind the body. Framing the analysis with non-dimensional Froude, Reynolds, and Archimedes numbers. The study reveals the dominant scaling laws and furnish predictive correlations for impact-driven flow motion and mixing. Different fluids and mixtures were prepared to study the behavior of falling solid object in accordance with the variations in viscosity and yield stress. Several methods such as image analysis techniques, and flow measurement were implemented using in-house algorithms and MATLAB software codes to extract the required information for solid object movement, deceleration, crown geometries, and pinch-off time. The outcomes indicate that the behavior of the sinking solid object reveals valuable information about the dependence of the criteria that play an important role in the dynamics of the sinking objects. Additionally, the dynamics of rising bubble is investigated. The dynamics and characteristics of bubbles vary due to the properties of both solid objects and the ambient fluid. The influence of flow rates is investigated in a wide range, which could be nominated as a proper representative of many of its applications.
Localization and tracking in imperfect mmwave systems with lower bound benchmarks
(2023) Tubail, Deeb Assad; Ikki, Salama Said
Radio localization and tracking have enormously grown in the fifth generation (5G) of cellular systems and are no longer limited to emergencies. Furthermore, the data obtained from these processes proves highly beneficial for cellular networks, offering advantages such as enhanced network control and more efficient resource management. Accordingly, this thesis investigates localization and tracking in 5G and beyond. In particular, it targets realistic circumstances where the theoretical assumptions of a perfect synchronous system and ideal transceivers no longer exist. In this thesis, we undertake the task of localizing and tracking objects in progressively challenging scenarios. Subsequent to each localization and tracking process in these scenarios, we offer a performance analysis tool, accompanied by the derivation of benchmark metrics. Notably, we establish the Cramer-Rao Bound (CRB) as the benchmark for localization assessment and introduce the Bayesian Cramer-Rao Bound (BCRB) as the benchmark for tracking evaluation. In the context of localization, the initial scenario involves localizing a mobile station (MS) equipped with a single antenna within a perfectly synchronized millimeter-wave (mmwave) multiple-input single-output (MISO) system implementing the orthogonal frequency division multiplexing (OFDM), taking into account hardware impairments (HWIs) occurring at both the base station (BS) and the MS. Subsequently, the localization task advances to a more intricate environment, where localization accuracy is compromised by non-line of sight (NLoS) effects caused by unknown position scatterers, in addition to the presence of HWIs. Continuing the exploration, the localization process is extended to an environment where it is implemented within an asynchronous reconfigurable intelligent surface (RIS) aided mmwave MISO system. Here, our focus shifts to achieving localization alongside synchronization in a RIS-aided mmwave MISO system that is subject to HWIs. As for tracking, we also delve into this aspect within both a perfectly synchronized mmwave MISO system and a RIS-enhanced mmwave MISO system. In the first system, tracking performance is notably hampered by the presence of HWIs. Specifically, we engage in range-direction tracking of the MS relative to the reference BS. Subsequently, we proceed to track the MS’s position concerning the reference BS. However, in the second RIS-aided mmwave MISO system, tracking accuracy experiences a decline owing to both HWIs and synchronization errors, as we focus on monitoring the MS’s position in this particular configuration. From a technical standpoint, the process of localization, tracking, and even joint localization-synchronization is carried out on the MS board. This is achieved by estimating the downlink channel parameters using a maximum likelihood (ML) estimator. Subsequently, the localization and the joint localization-synchronization tasks are finalized by inputting these estimated parameters into specific geometric equations that establish a connection between the estimated values and the MS’s position and clock drift relative to the reference BS. Regarding the tracking process, the estimated parameters are subjected to processing using the Kalman filter (KF) when the relationships between the measurements and tracked elements exhibit linearity. Conversely, when these connections display nonlinearity, the extended Kalman filter (EKF) is utilized to manage these parameters. Both KF and EKF execute tracking by combining the estimated parameters, which represent the measurements, with prior information pertaining to the transition model of the MS. During the evaluation phase, we determine the localization and synchronization boundaries by calculating the position error bound (PEB) and synchronization error bound (SEB) using the CRB as a reference. Therefore, the CRB serves as a mathematical benchmark for assessing both the localization and the joint localization-synchronization procedures. This benchmark is derived by mathematically inverting the Fisher information matrix (FIM) associated with these processes. To initiate this procedure, we first construct a model for the received pilot signal, which is utilized in the estimation of the downlink channel parameters. Subsequently, we compute the FIM for the estimation of these downlink parameters and then transform it into the FIM for the localization and joint localization-synchronization tasks. The assessment of tracking performance involves a comparison with the BCRB, which results in tracking error limits. The BCRB takes into account not only the valuable information obtained from received pilots but also the valuable information derived from understanding the transition model of the MS. As a result, we follow a similar series of steps as those outlined for localization to compute the FIM related to the measurements. Subsequently, we calculate the FIM matrix associated with the MS’s transition model. The combination of these two FIMs forms the Bayesian information matrix (BIM), which is mathematically inverted to yield the BCRB benchmark. In conclusion, we perform numerical experiments to assess our processes. The results obtained from these computer simulations analyze the level of accuracy achieved in localization and tracking across various suggested scenarios. This accuracy measured by simulation is juxtaposed with the established benchmarks. The findings from both the simulation accuracy and the benchmarks reveal the detrimental effects of HWIs on localization and tracking performance, and this deterioration is inversely proportional to the transceiver quality. An analogous negative effect is observed as a result of the reflected NLoS paths from scatterers with unknown positions. Furthermore, the asynchronous scenarios demonstrate that assuming perfect synchronization masks a portion of the degradation observed in localization and tracking accuracy. However, in these numerical experiments, we achieve the theoretical accuracy presented by CRB for localization and by BCRB for tracking when these processes are implemented with perfect transceivers conditional to negligible NLoS reflections. On the other hand, with non-ideal conditions, the numerical experiments show that applying the proposed Monte Carlo (MC) approach with KF and EKF leads to a significant enhancement in accuracy. Furthermore, we leverage the capabilities of the proposed machine learning techniques (MLT) to offer a streamlined and highly accurate solution that does not rely on prior models and statistics around the MS.