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How the Experiences of Education Professionals Working in Remote First Nations Schools Influence their Interest in, and Their Capacity to Teach Indigenous Youth Mental Health Literacy (MHL)
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This study explores how the experiences of education professionals working in remote First Nations schools influence their interest in and capacity to teach Indigenous Youth Mental Health Literacy (IYMHL). Grounded in Indigenous Research Methodologies (IRM), the research emphasizes respect, reciprocity, and relationality to understand the challenges and strategies educators employ in addressing mental wellness among Indigenous youth. Utilizing qualitative methods, interviews with education professionals from Cree First Nations schools in Northern Alberta revealed key themes such as the need for local Indigenous mental health professionals, land-based cultural learning, and the integration of Indigenous languages and practices in education. Findings highlight the critical role of culturally sensitive mental health literacy resources and the importance of co-creating curriculum with Indigenous educators. This research underscores the urgency of addressing mental health disparities among Indigenous youth through culturally informed educational practices
Dynamic Analysis and Stabilization of a Utility-Scale Hybrid PV-Wind-Battery Storage System
No full textRecently, hybrid power generation systems incorporating photovoltaic (PV), wind turbine (WT), and battery energy storage systems (BESS) have gained significant attention in utility-scale grid-connected applications. These systems increase overall energy production and enhance generation reliability. However, the analysis and stabilization of dc-link oscillations induced by interaction dynamics between the ac-side of the voltage-source converter and the energy sources on the dc-side at diverse operating conditions of the PV, WT, and BESS power characteristics curves are unexplored in existing literature. This research reveals that the complete hybrid system may experience severe low-, medium-, and high-frequency oscillation instabilities under certain operating conditions, which are associated with negative interactions between the weak grid's high dynamic impedance and the high dynamic resistances of the energy sources on the dc-link. To address these challenges, this research work develops 1) a novel hybrid approach based on analytical and one of the metaheuristic algorithms, the flow direction algorithm, to accurately extract the parameters and the dynamic resistance of different PV equivalent circuits and study their impact on the overall grid-connected system stability, 2) a comprehensive time-domain nonlinear model and a linearized state-space model for the dynamic analysis of a typical grid-connected VSC-based PV-only, WT-only, hybrid PV-WT, and hybrid PV-WT-BESS systems, 3) effective and simple active damping techniques for these grid-connected systems to reposition the unstable eigenmodes and reshape the dc-link transfer function, thereby maintaining the Nyquist stability criterion, and 4) a novel reduced-order modeling approach for a grid-connected hybrid PV-WT-BESS system to facilitate computationally efficient dynamic stability analysis. Detailed offline and real-time simulations validate the accuracy of the developed models and the effectiveness of the proposed stabilization methods in various typical operational conditions
Advancing Winter Road Surface Condition Monitoring through Deep Learning and Geostatistics
Get PDFMonitoring road surface conditions (RSC) during winter is crucial for transportation agencies to ensure road safety and effective winter road maintenance (WRM). Accurate RSC information helps determine the timing and location of WRM operations, assess treatment effectiveness, and manage contractor performance. However, current monitoring methods, such as stationary and mobile Road Weather Information Systems (RWIS), are limited by high costs and sparse deployment, leaving significant gaps in continuous and spatially detailed RSC coverage.
This thesis addresses these challenges by developing innovative methods that combine deep learning (DL) and geostatistics to estimate RSC variables between RWIS stations. The DL-based computer vision techniques automate RSC recognition from RWIS imagery, while geostatistical methods provide spatial interpolation of RSC data in unmonitored areas. Together, these methods aim to fill the gaps in existing RSC monitoring practices, offering a more comprehensive view of road conditions during winter weather events.
To automate the recognition of RSC from RWIS imagery, convolutional neural networks (CNNs) were employed, achieving up to 98.5% accuracy. Explainable AI (XAI) techniques were used to improve model transparency and reliability. For mobile RWIS images, a technique was developed to convert classified images into numerical Road Surface Index (RSI) values, representing friction levels. For stationary imagery, two DL-based models; namely, pix2pix generative adversarial networks (GAN) and semantic segmentation (SS), were designed to estimate snow coverage ratios (SCRs), achieving up to 99.3% accuracy in detecting drivable areas and over 93% accuracy in estimating SCRs.
To address the spatial limitations of RWIS data, geostatistical methods like regression kriging (RK) were employed to interpolate continuous RSC variables between RWIS stations. The RK method, enhanced with K-Means clustering for weather event characterization, produced accurate road surface temperature (RST) estimations with errors as low as 0.619°C. Additionally, a novel geostatistical method, nested indicator kriging (NIK), was developed to interpolate categorical RSC data, achieving average accuracy rates of 67.5% across test datasets. These techniques were rigorously tested on a dataset of over 20,000 images from Iowa's Interstate 35 (I-35) and Interstate 80 (I-80) highways, covering five years.
The results demonstrated the effectiveness of the proposed methods, offering significant cost savings by reducing the need for RWIS stations and improving WRM operations. The innovations developed in this thesis have been integrated into a user-friendly web application for real-time RSC monitoring and spatial mapping. The research provides WRM decision-makers with valuable tools to enhance safety, mobility, and efficiency during winter conditions, contributing to a more effective and sustainable transportation system
Aura and Agency: A Study of the Lienzo de Tlaxcala and its Reproductions
No full textCreated between 1550 and 1564, the Lienzo de Tlaxcala was a pictorial canvas that recounted the conquest of the Aztec Empire from the perspective of the Tlaxcalans—key Indigenous allies of the Spanish conquistadors. More than just a record of history, the Lienzo functioned as a political instrument that used its visual and material power to help the Tlaxcalans secure their special privileges. While three original versions of the Lienzo were made, each measuring approximately 5 by 2 meters, all have been lost or destroyed. Since then, understandings of the Lienzo have heavily relied on various reproductions that were created after
the original was lost. Importantly, while these reproductions maintain the core representational content, they employ drastically different mediums and formats to display it. This raises several important questions: What has been lost or gained in the reproductions of the Lienzo de Tlaxcala? How do the changes in medium and format affect the voices represented in the
document and the way they are perceived? What are the broader implications of these reproductions, and how do they shape our understanding of Tlaxcala’s account of the conquest? In this paper, I use two reproductions—Alfredo Chavero’s 1892 lithographic prints, and the 2017 digital reconstruction by the National Autonomous University of Mexico (UNAM)—to examine
how the various changes in the Lienzo’s physicality influence its interpretation. I first apply Walter Benjamin’s concept of the “aura” to explore how the reproductions affect the work’s essence and authority. Then, I use Alfred Gell’s theory of agency to analyze how the Lienzo’s power and agency change across the three versions, and how the relationships between the
documents and their audiences shift. Finally, I discuss the implications of these changes on the construction of collective memory and identity. I argue that the original Lienzo de Tlaxcala functioned as an active agent in shaping its own meaning and historical narrative—an agency diminished in both the Chavero and UNAM reproductions, which deconstruct the work and strip
it of its power with their changes in materiality and format, revealing how memory and history are continually reconfigured by the contexts in which they are recalled and represented. This study aims to underscore the complexity of memory, history, and the role of visual culture in shaping collective identity
Stochastic Power Flow and Transmission System Vulnerability Analysis Against FDIAs based on Quantum Computing
No full textThe recent advancements in quantum computing, encompassing both hardware innovations and algorithmic developments, have significantly elevated its potential for practical applications. This thesis leverages these advancements to tackle two critical challenges in modern power systems: stochastic power flow and cyber-security vulnerability analysis, especially pertinent to the increasing penetration of renewable energy sources (RES).
In order to address the first technical challenge, we propose a stochastic quantum power flow analysis (SQPFA) method. This method utilizes quantum amplitude estimation (QAE) and innovative quantum circuit designs to efficiently and accurately perform stochastic power flow analysis. The SQPFA method is evaluated based on IEEE 33-Bus and 123-Bus Test Feeders, demonstrating results consistent with traditional Monte Carlo simulations but with significantly improved computational efficiency. This showcases the substantial potential of quantum computing to help enhance the reliability and stability of power systems with high levels of RES integration.
The second challenge addressed in our research is the increasing threat of cyber-attacks, particularly false data injection attacks (FDIAs), which can bypass traditional bad data detection (BDD), leading to erroneous state estimations and potential system failures. In our initial study, we proposed the quantum vulnerable node location framework (QVNLF), which leverages the quantum approximate optimization algorithm (QAOA) to identify critical nodes within power systems accurately. These nodes, if compromised, could cause significant disruptions. This method was validated based on the IEEE 5-Bus and 9-Bus transmission systems, demonstrating that quantum computing could achieve comparable results to traditional algorithms, such as the Stoer-Wagner method, with enhanced computational efficiency.
Building on this foundation, our subsequent work introduced a stochastic quantum vulnerability analysis framework (SQVAF) to further bridge the gap between theoretical advances and practical applications. This framework extends the QVNLF by incorporating stochastic elements to better reflect real-world transmission systems, thereby providing a more robust analysis of system vulnerabilities under varying conditions. Case studies on the IEEE 5-Bus and 14-Bus transmission systems validate our approach, showcasing its superior performance in identifying vulnerabilities and mitigating cyber-security threats.
This research highlights the practical applications of quantum computing in enhancing the resilience and reliability of future smart grids. By addressing these two pivotal challenges, this thesis advances efforts toward building more reliable and secure power systems, paving the way for broader adoption of quantum computing technologies in power system analysis and beyond
The role of spatial visualization in primary mathematics and science: A content analysis of Alberta’s curriculum
No full textOtherworlds and Illusionary Spaces in Japanese Buddhist Tale Literature
No full textThis project focuses on the theme of otherworlds (ikai 異界) as presented in Japanese Buddhist didactic literature, namely setsuwa 説話 and otogizōshi 御伽草子, from the Heian (794-1185) and Kamakura (1185-1333) periods. In these tales, otherworlds appear in many forms, including as underworlds, underwater realms, or even illusions conjured by mischievous animals. Despite being abundant in tale literature, ikai are relatively understudied in English and Japanese secondary literature. Therefore, I investigate how otherworlds are used within these texts and how the characters in these stories interact with them. I find that, in both forms of didactic literature, otherworlds serve to teach many lessons. Including them can teach readers about the six realms (rokudō 六道) said to exist within Buddhist cosmology, and by writing of terrifying spaces, such as the Buddhist hells, ikai can be used as a warning against bad behavior. However, otherworlds themselves are often used as models for the wider cycle of death and rebirth known as saṃsāra. Even in some of the simplest otherworld tales, protagonists will experience a sequence of events reminiscent of death, rebirth, life, enlightenment, and escape into final nirvāṇa—all while they are within whatever otherworld they had been exposed to or when moving between realms. The otherworlds are often described in ways that make them seem similar to the human realm, thus rendering the human realm as an ikai that, too, can be escaped through the buddha path. Though the protagonists rarely become truly enlightened by the end of the story, their experiences within otherworlds demonstrate the steps to escaping into parinirvāṇa in an entertaining manner that might be more memorable to and understandable by lay audiences. I also find that women’s interactions with otherworldly spaces differ from those of men. Women are often imagined as luring men into ikai and, in some cases, foxes disguised as human women even conjure illusionary spaces. This is likely due to conceptions about women’s bodies and salvation that were prevalent at the times in which these didactic tales were compiled, in which women were imagined as being inherently seductive to men and the embodiment of attachments. Many monastics believed that women could not attain enlightenment without first transmigrating into the body of a man. In this way, women became the ideal character archetype to lead men into another world, thus keeping them in saṃsāra. The illusions created by foxes also resemble the state in which intermediary spirits are suspended while residing within their mother’s womb. In other cases, when women themselves enter an otherworld, those with more “masculine” personalities (such as being dedicated to the dharma and confident) are treated similarly to men by beings living within the ikai. Women who are more sexually active and who have borne children are treated more harshly and find it difficult to leave the other world, even if they devoutly follow the dharma. In contrast, even impious men are given better aid than pious, but sexually active women, and are easily rescued from ikai. In other words, through the difficult escapes from otherworlds, these didactic tales teach that women would have to practice harder than men. Otherworlds, therefore, not only provide an entertaining and easy to understand model of the large cycle of saṃsāra, they also teach both men and women what they must do to escape suffering