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Design, Synthesis, and Characterization of Multimetallic Complexes Supported by an Imidazopyrimidine-Based Trinucleating Ligand
No full textTransition metal catalysis has revolutionized chemical synthesis for decades and has
allowed for the development of several Nobel prize-winning chemical reactions and processes.
These catalysts, however, usually rely on the use of rare Earth metals such as platinum-group
metals, mainly palladium, leading to economic and sustainability concerns. Recent studies on the
use of Earth-abundant elements nickel, cobalt, and copper have revealed that these metals have
the potential of offering low-cost alternatives to the traditional catalysts. Furthermore, these metals
can access many more states, allowing for new and complementary reactivities to be achieved.
Whilst transition metal catalysis is a large and impactful field, the majority of known
catalysts are monometallic in nature. A compelling yet much underexplored area is the use of
multimetallic complexes. Several studies and reviews have highlighted the beneficial effect of
having multiple metal centers held in proximity. These sorts of systems often display improved
catalytic performances over their monometallic counterparts. Synergy or metal-metal
cooperativity between the centers is usually responsible for these observations, sometimes
allowing for multielectron processes that are simply not possible with traditional monometallic
catalysts. In terms of trimetallics, there is a paucity of ligand systems that can reliably produce a
precise and controlled arrangement of the three metal centers in a way that is useful in catalysis.
This is due to most relying on flexible organic frameworks tied to a symmetric node, additionally
excluding them from heterometallic applications.
Herein is reported a new trinucleating ligand framework, bpipp, specifically designed to
enforce close proximity among three metal centers upon complexation. Based on the inherently
unsymmetric imidazopyridmine backbone, the ligand features a tridentate pincer-like binding
pocket with two additional bidentate binding pockets. This approach utilizes scalable synthetic
methods to create a rigid ligand scaffold that precisely controls the spatial arrangement of the
metals. The versatility of this ligand is demonstrated through the synthesis of several trimetallic
complexes of Ni(II), Cu(II), Co(II); fully characterized by NMR spectroscopy, ESI-HRMS, and X
ray crystallography. Notably, our ligand design achieves remarkably short metal-metal distances
ranging from 3.3–3.5 Å, significantly closer than most reported trimetallic systems. This structural
feature establishes an ideal platform for investigating genuine three-metal cooperative effects in
catalysis
Computer Vision Based High-Fidelity Mapping and 3D Reconstruction for Civil Infrastructure Inspection
No full textGlobally, infrastructure is deteriorating due to aging structures and delays in timely and effective rehabilitation. As a result, there has been a growing demand for efficient and scalable methods to assess the condition of civil infrastructure. Traditional inspection practices, which rely heavily on manual visual assessments, are time-consuming, labour-intensive, and prone to human error. As a result, there is growing interest in automating traditional inspection processes. A common approach involves using 3D reconstructions of civil structural components through off-the-shelf Structure-from-Motion (SfM) software to create digital twins for measurement and analysis. However, this method faces several challenges. First, the black-box nature of current SfM software used for 3D reconstruction is not optimized for the visually degenerate surfaces common in civil infrastructure and lacks transparency for error diagnosis in case of a failed reconstruction. Secondly, existing methods often fail to provide end-to-end support for extracting measurements that comply with structural inspection manuals.
This thesis proposes an open-access, end-to-end framework for enabling vision-based structural inspection through high-fidelity 3D reconstructions. The motivation is to address key technical and scientific challenges in adopting computer vision tools for infrastructure assessment by providing field-deployable and standards-compliant solutions that enhance both visualization and quantification of structural conditions. The methodologies developed in this thesis support inspections needed at small, medium and large-scales of structural components.
The first and second parts of this thesis address small-scale inspection. In the first part, high-fidelity reconstructions from smartphone-based LiDAR sensors are utilized to extract concrete surface roughness profiles. Point cloud processing methods are calibrated against existing subjective field tools used by inspectors to ensure compatibility and enable classification of roughness profiles within current inspection frameworks.
The second part addresses deployment challenges by introducing a reconstruction tool that only uses images. This enables small-scale reconstruction in environments where LiDAR or high-end equipment is unavailable. By removing hardware constraints and validating the proposed tools through field deployment, this thesis demonstrates the practical feasibility of an open, vision-based inspection workflow for performing surface roughness measurements.
The third part focuses on medium-scale inspection. An image-based 3D reconstruction pipeline is developed, followed by integration with an interactive segmentation algorithm. The AI-based segmentation method is integrated with 3D models to detect and quantify a common defect, concrete spalling, in accordance with structural inspection standards.
Finally, a large-scale multi-resolution map (MRM) reconstruction workflow is developed for constructing 3D maps with varying resolutions by integrating LiDAR sensor-based 3D maps (coarse resolution) with maps built from images (fine resolution). This method uses a novel image-based localization algorithm to precisely align two maps into a cohesive 3D point cloud. To facilitate MRM, an in-house, cost-effective and portable backpack-based scanner and mapper is designed to collect large-scale colourized LiDAR maps.
Experimental results are presented for each method, including field tests, demonstrating the accuracy and utility of the proposed system for real-world inspections. The major contribution of this work is bridging the gap between academic research and practical implementation in infrastructure inspection, advancing toward a more intelligent, scalable, and accessible inspection paradigm
How Architectural Style, Height, and Complexity Influence Perceived Oppressiveness in Urban Spaces
No full textThe design of urban environments strongly influences psychological experience, yet research on how building form influences affective responses remains limited. This study used immersive virtual reality to examine the combined effects of architectural style (modern vs. contemporary), building height (low-, mid-, and high-rise), and façade complexity (low, medium, high) on affective perceptions of urban streetscapes. Forty-nine participants explored 18 virtual environments and rated each on oppressiveness, openness, restoration, arousal, and environmental liking. Results showed that greater building height consistently increased perceived oppressiveness and arousal while reducing openness, stress restoration, and liking. Greater façade complexity increased preference, openness, and restoration, and buffered the oppressive effects of high-rises, particularly in modern-style settings. Participants also expressed a clear preference for low- and mid-rise settings over high-rises. These findings reiterate and expand on the restorative and aesthetic benefits of architectural complexity and the value of human-scale design in supporting psychological well-being in urban dwellings
Comparative Effect of Herceptin and Its Biosimilar: Transcriptomic and Proteomic Insights from HER2-Positive Cancer Cell Lines
No full textThe first patent for the therapeutic monoclonal antibody Herceptin expired in 2019,
opening the door for the development of biosimilars. Biosimilars are copies of the originator
product that must demonstrate biosimilarity for regulatory approval. This is a very difficult
task. Antibodies are large molecules that undergo extensive post-translational modifications
(PTMs), which affect their potency, stability, binding affinity, and immunogenicity. Batches
of the originator products often vary in their PTM patterns, making structural similarity very
difficult to prove. Despite these challenges, most biosimilar development workflows rely on
targeted wet-lab assays to assess structural similarity, purity, and efficacy, often without
addressing the full complexity of the task.
Herceptin targets the surface receptor HER2 (human epidermal growth factor receptor 2)
on breast cancer cells, inducing global cellular changes including cell arrest, programmed
cell death, reduced motility, and angiogenesis. The complete mode of action is under
investigation, and the development of resistance to Herceptin is common and complex. Due
to these gaps in knowledge and complexity of Herceptin’s effect, untargeted omics
techniques, such as transcriptomics and proteomics, are ideally suited for functionally
comparing biosimilars to their originator product.
Therefore, the primary goal of this thesis is to evaluate whether transcriptomics and
proteomics experiments are useful for investigating the impact of the biosimilar on target
cells compared to the originator product. To this end, Herceptin’s effect on the breast cancer
cell lines BT474, SKBR3 and MCF7 will be compared to a biosimilar, called Apotex-
Trastuzumab (ApoTras), provided by Apobiologix (Toronto, Canada). This analysis will
allow us to investigate the sensitivity of the methods in detecting differences and
determining how many of the known effects can be identified. This work will provide
valuable insights into the use of untargeted omics approaches for investigating the functional
similarity of a biosimilar to its originator product
Evaluating remote sensing and modeling approaches for estimating net ecosystem exchange in Canadian peatlands
No full textPeatlands represent a type of wetland, that has accumulated a layer of organic material or peat, resulting in high organic carbon (C) accumulation in this ecosystem. Peatlands hold up to one-third of the global organic C stock, and specifically peatlands located at high latitudes in the Northern Hemisphere, or northern peatlands, store a large proportion of the of the total peatland organic C stock. However, this organic C may be in jeopardy, as warmer temperatures may lead to increases in both the C uptake and output. There is some uncertainty as to how northern peatlands C sink function will be impacted by climate warming, and conventional models of peatland C cycling have been constrained to site-specific applications rather than national-scale analyses. Moreover, in-situ measurements are limited in northern peatlands, due to the remoteness of these sites, but also due to equipment limitations under low temperature and light conditions. In this thesis, I specifically looked at one component of C flux, the net ecosystem exchange (NEE) of CO2. To enable forecasting of NEE of CO2 fluxes in peatlands under future scenarios, or to generate real-time estimates where no in-situ measurements exist, machine learning algorithms trained on in-situ CO2 fluxes from the eddy covariance (EC) technique must be applied. Remotely sensed or gridded climate data products represent potentially important inputs to these modeling applications, as they are widespread both geographically and temporally enabling flux estimation for broader geographic domains.
In Chapter 2, I explored the possibility of using the remotely sensed and modeled Soil Moisture Active Passive Level 4 Global Daily EASE-Grid Carbon NEE (SMAP-NEE) data product to determine NEE in Canadian peatlands. I acquired nine years (2015–2023) of SMAP-NEE data for five peatlands. I also acquired a subset of year-round eddy covariance NEE (EC-NEE) measurements within this time frame at each of the five peatland sites. The analyses showed that the SMAP-NEE data product reports a stronger growing season (GS) sink and a weaker non-growing season (NGS) source than the EC-NEE measurements. As a result of this finding, I used the relationship between SMAP-NEE and EC-NEE to produce a Corrected-SMAP-NEE dataset, which provides an estimate of seasonal and annual CO2 budgets. The data analyses of the Corrected-SMAP-NEE dataset showed that NGS CO2 emissions represent a variable proportion (33%–256%) of the GS CO2 uptake, and when these NGS emissions were accounted for, the annual CO2 sink strength was reduced proportionally. Furthermore, this study showed that longer growing seasons were consistent with greater annual net CO2 uptake at these five peatland sites from 2015-2023. The findings highlight the importance of considering the NGS when evaluating annual northern peatland C budgets. This chapter also provides evidence that existing algorithms leveraging remotely sensed and gridded climate data products to model NEE need improvement for peatlands.
In Chapter 3, I compiled year-round measurements of EC-NEE from 15 Canadian peatland sites and coupled these target data with 34 hydroclimatic predictor variables (features) from remote sensing and gridded climate data products. The models were trained, validated, and tested using four algorithms: ElasticNet Regression (EN), Light Gradient-Boosting Machine (LGBM), Random Forest Regression (RFR), and Support Vector Regression (SVR). A comprehensive feature importance and selection workflow including hierarchical clustering, Gini importance, and minimum redundancy maximum relevance (mRMR) analysis was followed. Model performance stabilized at eight features, which were (relative importance shown in parentheses): evapotranspiration (40%), shortwave radiation (19%), burn area index (11%), normalized difference snow index (10%), snow water equivalent (7%), climate water deficit (4%), wind speed (4%), and soil moisture (4%). I found the best performing model to be the RFR model with these eight features (R2 = 0.76; RMSE = 0.31 g C m−2 day−1). I also assessed the generalizability and transferability of the top-performing model via a leave-one-ecoregion-out sensitivity analysis as well as on six external validation sites. The RFR model had the highest generalizability within the Taiga Plains ecoregion and the lowest generalizability within the Boreal Plain ecoregion. When testing the model on the external validation sites, performance metrics were comparable to the internal testing data for sites outside of the ecoregions represented in the training data. The findings demonstrate that the eight-feature models can be confidently upscaled to national extents, offering a clear pathway to improve Canada’s spatially explicit CO2 emission inventories
The Self-Reference Effect in the Visual and Auditory Modalities: Effects of Referent and Valence on Memory Performance
No full textPeople tend to better remember information that has been encoded in reference to the self than information that pertains to someone else, a phenomenon termed the self-reference effect (SRE). It is also believed that this bias for self-relevant information is selective, such that healthy adults prioritize the encoding of self-positive relative to self-negative information (a self-positivity bias). Depressed individuals, on the other hand, are believed to display a self-negativity bias, whereby they remember more negative than positive information about themselves. Previous studies have assessed these two biases using the Self-Referential Encoding Task (SRET). In this task, participants first endorse, using a yes/no judgement, visually presented positive and negative trait adjectives, as either accurately representing themselves or another known character (e.g., Harry Potter). This task is then followed by surprise memory tasks for these adjectives. After the task, when depression is a variable of interest, participants complete a self-report measure of depression. In our study, depression was assessed in a non-clinical sample using the CES-D self-report measure. Participants classified as "depressed" were those who reported levels of depressive symptoms superior to 16, based on the measure’s established cutoff. To our knowledge, no research using the SRET has examined whether these two biases also exist when the information is presented through the auditory modality. Given that self-relevant information is often encountered through spoken language in daily life, it is important to explore how these biases operate in the auditory modality. In the present study, participants were assigned to complete the SRET in either the visual (n=176) or auditory (n=176) modality. Results confirmed a significant SRE in both modalities and did not reveal an interaction between SRE and modality. Contrary to expectations, there was no evidence of a self-positivity bias, nor were there any differences in the pattern of results for depressed (n=186) vs. non-depressed (n=166) participants in the recognition task, although a significant decrease in positivity bias was found for depressed individuals during the endorsement task. Overall, these findings suggest that the SRE is consistent across modalities. However, the absence of both a self-positivity bias in healthy individuals and a self-negativity bias in depressed individuals diverges from previous research. Given the large in-person sample size and highly controlled stimuli in this study, these null effects warrant further investigation into the valence-related memory biases previously reported
Towards a Balanced Lens: Strengths-Based Psychoeducational Assessments in Canadian Schools
No full textPsychoeducational assessments are commonly used in schools to evaluate concerns relating to cognitive domains (e.g., academic, attention, memory, etc.). After the assessment, the goal for the student and their family may be to have a better understanding of themselves, how they function within their world, and the possibility that they can recognize and use their strengths. Unfortunately, social-emotional-behavioural outcomes seem to worsen over time through deficit-focused lenses as negative experiences tend to be more salient than positive experiences. Grounded in positive psychology and resilience perspectives, strengths-based approaches (SBA) to assessment offer an alternative approach that highlights students’ assets and resilience, while paying equal attention to areas of challenge. Studies show that despite adopting strengths-based tools and strategies, client and family engagement (e.g., retention and satisfaction) is truly dependent upon if the clinician taking this SBA recognizes its worth. While SBA is conceptually supported in the literature, little is known about how aware school psychologists and psychological associates in Canada are about SBA, and how they implement these practices in their assessment work. The present study surveyed 42 Canadian school-based clinicians to examine beliefs and practices related to SBA, as well as whether clinician characteristics predict SBA practice. Multiple regression models indicated that stronger endorsement of SBA beliefs and greater years of experience (i.e., later career stage) significantly predicted greater use of SBA practices. These results suggest that both clinician attitudes and continued professional development are key to integrating SBA within psychoeducational assessments. This study sheds light into assessment practices in Canadian schools and provides a foundation for future research and training to promote more balanced and empowering assessments for students
Kinematic and Thermodynamic Effects on Particle Clustering in Turbulent Flows
No full textParticle-laden flows are ubiquitous in numerous state-of-the-art engineering applications such as dispersed metallic particle combustion, particle solar collectors, and cold spray manufacturing. In such thermal applications, ensuring a uniform distribution of particles within a carrier fluid is critical, yet inertial particles intrinsically cluster in turbulent flows. In past investigations, particle clustering was treated as a purely kinematic problem, and typically, the variable temperature effects of both the particles and the gas were neglected. This is perplexing, as the increase in carrier gas temperature increases viscosity, which concomitantly enhances the coupling force between the fluid and the particles. Thus, it is plausible that particle clustering is influenced not only by kinematics but also by local thermodynamics. This line of thinking has motivated the scientific questions that underpin the present work.
To understand the role of thermodynamics on particle clustering, direct numerical simulations (DNS) were carried out to analyze the clustering behavior of heated dispersed particles in homogeneous isotropic turbulence (HIT) and subsonic jet flows; the study was then extended to explore particle-laden supersonic jet flows. In these analyses, the continuum and particulate phases were modeled with the Eulerian and Lagrangian point-particle approaches, respectively. The fluid-particle energy was coupled with two-way coupling (TWC), while momentum exchange between the two phases was modeled with one-way coupling (OWC) and TWC. The analysis of the simulations led to the discovery of a novel particle clustering mechanism called viscous capturing (VC), in which preformed particle clusters create hot spots. The higher temperature of the gas in these hot spots led to regions with higher local viscosity, termed viscous clouds. The increased drag on the particles in the viscous clouds makes it hard for the clustered particles to leave the clusters, and it also aids in capturing more particles passing through the viscous clouds. This ultimately enhances particle clustering. An opposite particle clustering behavior was observed in a carrier phase with liquid-like viscosity (viscosity decreases with temperature rise), where particle heating resulted in superior particle dispersion.
Furthermore, the VC effect was found to increase with the rise in particle loading density. This is because, at higher particle loading density, the two prerequisites of VC are satisfied: (1) particles should be clustered before particle heating is initiated, and (2) these clusters should be large and unevenly distributed in the flow. In these viscous clouds, the fluid-particle and particle-particle slip velocities were found to be well-correlated, which further aids in keeping particles clustered. Apart from these conditions, particles should be uniformly heated for VC to take effect.
The HIT helps to isolate the effect of turbulence on particle clustering but lacks generalizability or the mean shear of more relevant particle-laden flow configurations, such as a jet flow. In subsonic jets, even a moderate rise in local gas viscosity can initiate VC, which results in higher clustering of heated particles as compared to unheated particles. This is because the introduction of particles within the jet core, which causes particles to be close to each other, also satisfies the two prerequisites of VC and retains particles within the jet. Thus, instead of individual viscous clouds compared to the HIT case, the entire central region of the jet acts as a viscous cloud. Even the particles with a higher radial velocity component remained within the jet, as they were unable to cross the sharp temperature gradient at the outer edge of the jet due to their propensity to collect at the location of a sharp scalar gradient. Thus, heated particles are essentially restrained within the jet.
On the other hand, in supersonic jet flows, it was determined that particles of different inertia have a distinct axial location where they start dispersing radially (xᵣ). This xᵣ is marked with a local Stokes number of St* ≈ 0.6. A new metric was also introduced to estimate xᵣ for practical applications. Particle dispersion was also influenced by the compressibility effects, where larger particles were found to have a higher propensity to settle in high-density gradient and dilatation regions of the flow
Learning-Based Stability Certification and System Identification of Nonlinear Dynamical Systems
No full textIn recent decades, by taking advantage of the abundance of sensory measurements, learning-based methods have been prevalent and shown their effectiveness in tackling challenging or intractable problems for classical approaches in systems and control. For instance, many systems with complex nonlinearities, high-dimensional state spaces, or unknown dynamics cannot be effectively handled by classical mathematical tools, and computing stability certifications for such systems is often intractable. This thesis aims to construct systematic approaches to perform system identification tasks and learning-based Lyapunov functions for nonlinear dynamical systems, with some extensions to optimal control.
The first aspect of this thesis is to develop an efficient method based on a special feedforward neural network structure, an extreme learning machine, to compute stability certificates for nonlinear systems by solving linear PDEs when the dynamics are accessible. Differing from the typical neural network-based approaches that require training on high-performance computing platforms, one only needs to solve a convex optimization problem. On top of that, the proposed method can also be used to efficiently solve the notable HJB equation via policy iteration to obtain optimal control policies for nonlinear systems. The second aspect of this research is to tackle these issues for nonlinear systems with (partially) unknown dynamics. We first show that with two feedforward neural networks, the unknown system and a Lyapunov-based stability certificate can be learned simultaneously. With the help of satisfiability modulo theories (SMT) solvers, the resulting Lyapunov function can be formally verified to provide stability certificates for the unknown nonlinear system.
Alternatively, in the past two decades, the Koopman operator and its generator have demonstrated advantages in identifying discrete-time systems and continuous-time systems, respectively, requiring significantly less data while achieving better performance than most existing classical methods. For unknown continuous-time dynamical systems, we propose a novel resolvent operator-based learning framework to learn the Koopman generator, which is a linear operator that describes the infinitesimal evolution of the Koopman operator. The learned generator, thereafter, can be used to identify the vector field of the nonlinear systems. Moreover, with the learned high-accuracy Koopman generator, we can also construct a Lyapunov-based stability certificate for the unknown nonlinear system in the same function space. By formulating the linear PDEs as a linear least squares problem, Lyapunov functions can be computed efficiently. The learned Lyapunov functions can be formally verified using an SMT solver and provide less conservative estimates of the region of attraction, compared to existing methods.
Taken together, these contributions provide a coherent pathway that begins with model-based stability certification computation and continues to fully data-driven system identification and thereafter computing Lyapunov-based stability certificates
Using eye tracking to study the takeover process in conditionally automated driving and piloting systems
No full textIn a conditionally automated environment, human operators are often required to resume manual control when the autonomous system reaches its operational limits — a process referred to as takeover. This takeover process can be challenging for human operators, as they must quickly perceive and comprehend critical system information and successfully resume manual control within a limited amount of time. Following a period of autonomous control, human operators’ Situation Awareness (SA) may be compromised, thus potentially impairing their takeover performance. Consequently, investigating potential approaches to enhance the safety and efficiency of the takeover process is essential. Human eyes are vital in an individual’s information gathering, and eye tracking techniques have been extensively applied in the takeover studies in previous research works. The current study aims at enhancing the takeover procedure by utilizing operators’ eye tracking data. The data analysis methods include machine learning techniques and the statistical approach, which will be applied to driving and piloting domains, respectively.
Simulation experiments were conducted in two domains: a level-3 semi-autonomous vehicle in the driving domain and an autopilot-assisted aircraft landing scenario in the piloting domain. In both domains, operators’ eye tracking data and simulator-derived operational data were recorded during the experiments. The eye tracking data went through two categories of feature extractions: eye movement features linked predominantly to fixation and saccades, and Area-of Interest (AOI) features associated with which AOI the gaze was located. Eye tracking features were analyzed using both traditional statistical techniques and machine learning models. Key eye tracking features included fixation-based metrics and AOI features, such as dwelling time, entry count, and gaze entropy. Operators’ SA and takeover performance were measured by a series of domain-specific metrics, including Situation Awareness Global Assessment Technique (SAGAT) score, Hazard Perception Time (HPT), Takeover Time (TOT) and Resulting acceleration.
Three research topics were discussed in the current thesis and each topic included one driving study and one piloting study. In topic 1, significant differences in eye movement patterns were found between operators with higher versus lower SA, as well as between those with better and worse takeover performance. Besides the notable differences in various Area-of-Interests (AOIs) across three pre-defined Time windows (TWs), in the driving domain, drivers with a better SA and better takeover performance showed inconsistent eye movement patterns after the Takeover Request (TOR) and before they perceived hazards. In the piloting domain, pilots with shorter TOT showed more distributed and complex eye movement pattern before the malfunction alert and after resuming control. During the intervening period, their eye movements were more focused and predictable, indicating fast identification of necessary controls with minimal visual search. In topic 2, significant differences in eye movement patterns were observed between younger and older drivers, as well as between learner and expert pilots. As for driving domain, older drivers exhibited more extensive visual scanning, indicating difficulty in effectively prioritizing information sources under time pressure. In piloting domain, expert pilots not only allocate more attention to critical instrument areas but also dynamically adjust their scanning behavior based on the current tasks. In topic 3, machine learning models trained on eye tracking features successfully performed binary classification for both SA-related and takeover performance related metrics. Model performance was evaluated using standard classification metrics, including accuracy, precision, recall, F1-score, and Area Under the ROC Curve (AUC).
Finally, comparisons were made across Topics 1 and 2, as well as between the driving and piloting domains. The results suggest that better operators can flexibly adapt their gaze strategies to meet task demands, shifting between broad visual scanning and focused searching when appropriate. This shift in patterns underscores the importance of accounting for the specific Time window (TW) when interpreting operators’ eye movements. Overall, this thesis advances the understanding of different eye movement patterns during the takeover process by exploring a range of eye tracking features. The findings support the development of operator training programs and the design of customized interfaces to enhance the safety and efficiency of takeover performance