From Oral Traditions to Digital Landscapes: Virtual Reality as a Tool for Revitalizing Indigenous Languages

This thesis investigates the role of virtual reality (VR) as an innovative tool for Indigenous language revitalization, emphasizing its potential to bridge generational and geographical gaps in language transmission. Centered on the Multimodal Indigenous Knowledge Systems (MIKS) framework, the study explores how VR can provide immersive, culturally rich environments that support language learning and cultural connection. Grounded in the principles of embodied cognition, the research highlights how VR can integrate traditional practices, stories, and landscapes to create engaging, contextually relevant learning experiences. The study examines the perspectives of VR creators, educators, Elders, and youth participants through interviews, focus groups, and workshop data, shedding light on VR's emotional, experiential, and motivational impacts. It also addresses challenges, including technological accessibility, cultural sensitivity, and the need for community-driven development. Findings demonstrate that VR may foster a sense of belonging and cultural pride while supporting language retention and engagement, particularly among Indigenous youth. This research underscores the significance of technology in advancing Indigenous language and cultural revitalization by situating these findings within the broader context of the United Nations Decade of Indigenous Languages and Canada’s Truth and Reconciliation Calls to Action. The results contribute to a growing body of scholarship on digital tools for education, providing actionable recommendations for culturally sensitive VR development and future research in this emerging field

Advancing geotechnical site investigation techniques by mitigating uncertainties in active Multichannel Analysis of Surface Waves (MASW) and integrating it with the Standard Penetration Test (SPT)

The Multichannel Analysis of Surface Waves (MASW) is a widely used geophysical technique for subsurface shear wave velocity (Vs) profiling. MASW can be categorized into two types based on the seismic source: Active MASW (A-MASW) and Passive MASW (P-MASW). A-MASW uses controlled sources, such as a sledgehammer, while P-MASW relies on ambient sources, such as traffic noise. A-MASW offers several advantages, including greater flexibility and control over source parameters like energy, location, and timing. It also provides superior signal-to-noise ratios (SNR) and higher resolution compared to P-MASW, making it a more reliable method for geotechnical characterization. This rapid, cost-effective technique is applicable to a wide range of soil and rock materials, with minimal environmental impact, and can generate 1D, 2D, and 3D Vs profiles to a depth of approximately 50 meters. However, despite these benefits, A-MASW, as a geophysical method, is subject to certain uncertainties, like near-field effects, analysis ambiguities, and the need for site-specific configurations. Further, the lack of standard guidelines for acquisition and analysis makes it challenging to get repeatable results from A-MASW. These limitations can compromise the accuracy, repeatability, and overall effectiveness of A-MASW. Hence, a thorough understanding of potential sources of uncertainties in A-MASW, their mitigation measures and optimal setups is essential to apply A-MASW properly and get reliable results from it. A-MASW can serve as a valuable complement to conventional geotechnical tests, enhancing subsurface investigations. The Standard Penetration Test (SPT) is a well-established geotechnical test for evaluating subsurface material strength through penetration resistance (N-value). Similar to other conventional geotechnical tests, SPT provides localized data at specific test points only and requires multiple tests for detailed site information. This can be time-consuming for large areas. Further, even with a dense borehole array, there is always a risk of missing critical lateral variations when interpolating data between boreholes. Determining the optimal borehole locations and the number of SPT tests required for comprehensive and reliable site characterization is another big challenge due to subsurface uncertainties. Integrating A-MASW with SPT offers a promising solution to overcome these challenges. The integrated approach can also help mitigate uncertainties in MASW. Soil cuttings, samples, subsurface information, and SPT N-values obtained from SPT can guide the selection of optimal field, acquisition, and analysis parameters for Active MASW (A-MASW), while also validating its results. Furthermore, the consistent and powerful energy from an automatic eSPT (electronic SPT, which records data digitally) hammer can generate robust seismic waves with longer wavelengths, enhancing the quality of MASW data. The research is divided into two parts: (1) investigating the uncertainties and providing practical guidelines for A-MASW application, and (2) proposing a new data acquisition approach for 2D MASW to develop an integrated eSPT-based MASW method for more reliable and comprehensive geotechnical site investigation. For the first part of the research, a comprehensive review of existing A-MASW literature and case studies was conducted to identify potential sources of uncertainty, their impacts, and mitigation strategies, focusing on geotechnical applications. Based on these findings, best-practice guidelines were recommended for selecting optimal field, acquisition, and analysis parameters to ensure reliable A-MASW results. For the second part of the research, pilot field studies were conducted at two different sites in Edmonton, Alberta to propose a new methodology for integrating A-MASW and eSPT data to enhance subsurface characterization while addressing the uncertainties inherent in A-MASW applications. At the first site, 2D MASW data were collected with a 10-lb sledgehammer using three methods: standard roll-along, moving source method, and proposed fixed-source method. The results from the new method were validated against other two methods, as well as SPT N-values and borehole logs. At the second site, data were collected using standard roll-along technique (sledgehammer) and proposed method (automatic eSPT hammer). The results from this integrated approach were then compared and validated against those from the standard technique, borehole logs, and SPT N-values along the survey line. The findings demonstrated that the eSPT-based MASW method produced higher-resolution dispersion images and more reliable information at greater depths, while requiring less time and effort compared to the standard roll-along technique. This research is a proof of concept that highlights the potential of the integrated eSPT-based MASW method to improve the comprehensiveness, reliability, and efficiency of subsurface investigations

Characterization of Mechanical and Thermal Properties of Highly-Cemented Edmonton Clay Subjected to Freezing/Thawing

Deep soil mixing (DSM) with cement has been utilized to enhance soft ground and stabilize problematic soils worldwide and has become more commonly used in cold regions, which requires a thorough understanding of the behavior of DSM-treated natural soils (soilcrete) exposed to severe cold environments. Previous research on soilcrete was concentrated on determining the performance of lightly-cemented soilcrete under temperate conditions. Therefore, the present thesis is aimed at investigating the performance of soilcrete with a high cement content (over 20%) under the freezing/thawing (F/T) cycles, including physical, mechanical, hydraulic, thermal, and microscopic properties. In the present study, cylindrical soilcrete specimens were prepared in the laboratory by mixing Edmonton clay with high contents of ordinary Portland cement. The uniaxial compressive and tensile behavior of soilcrete with/without F/T cycles were examined with unconfined compression strength (UCS) and tensile tests on specimens cured for 1 to 300 days after 1 to 20 F/T cycles at freezing temperatures from ¬−2 to ¬−20 °C. The results showed that the compressive and tensile strength of soilcrete increased with curing age and decreased significantly with F/T cycles. Additionally, the lower temperature caused more severe damage to the samples than the number of cycles. The mechanical properties of soilcrete in isotropically consolidated undrained (CIU) triaxial tests were determined and the permeability was measured by conducting tests on soilcrete specimens with a cement content of 22%. Meanwhile, the damage of F/T cycles on the solids phase alone was examined with UCS tests on dry soilcrete samples. Results of CIU tests indicated that the cohesion of soilcrete decreased with the declining freezing temperature, while the friction angle remained nearly unchanged after F/T cycles. In addition, the permeability of soilcrete showed a significant increase after F/T cycles and the impact of lower freezing temperatures on permeability was more noticeable than that due to the number of cycles. UCS results of dry samples showed that the damage from F/T cycles was negligible, probably confirming that F/T damage on soilcrete was primarily due to the water phase of specimens. The thermal conductivity of soilcrete has seldom been studied in the literature. The thermal conductivity of soilcrete under a cold environment was estimated using a temperature control system, which mimics a radial heat transfer flow and uses dry sand with a known thermal conductivity as the medium. Temperature distribution in soilcrete and sand confirmed that a steady state was achieved. The thermal conductivity of soilcrete at various freezing temperatures was calculated based on the temperature distribution. Thermal conductivities of soilcrete obtained in the present study were in line with those values determined using empirical equations in the literature and back-analyzed from finite element simulations. The microstructure of soilcrete after F/T cycles at different freezing temperatures was investigated using the X-ray computed tomography scanning method. Results showed that the volumetric cavity ratio of the soilcrete changed significantly after the F/T cycle and increased with the declining temperature. The cavity was divided into planar cracks and bulky holes based on their geometry. The volumetric distribution of planar cracks was illustrated and the equivalent diameter distribution of bulky holes varied with temperature

Application of Distributed Dislocation Technique in Heat Conduction and Multiphysical Problems of Cracked Structures

The use of smart materials, particularly piezoelectric materials, has seen significant growth due to their unique capabilities in sensing, actuation, and energy harvesting. However, these materials are frequently subjected to various thermal loads, necessitating robust fracture criteria for ensuring their structural integrity. The primary objective of this research is to develop an analytical framework to analyze the fracture behavior of smart materials, particularly piezoelectric materials, and to provide insights for the design of smart devices subjected to different thermal loading conditions. Using the Distributed Dislocation Technique (DDT), this work aims to provide a deeper understanding of the fracture behavior of structures with multiple cracks of different spatial distribution patterns, investigating how thermal, mechanical, and electrical fields affect crack propagation. The research begins by examining a single curved crack in a piezoelectric plane under general steady state temperature loading. The effects of heat source location, loading parameters, and crack geometry on stress intensity factors (SIFs) are thoroughly analyzed. Building on these insights, multiple cracks in a finite-sized, piezoelectric half-plane are studied, focusing on crack interaction, boundary effects, and crack angles on the multiphysical response of cracks under thermal loading conditions. In the subsequent phase, the research extends to study the transient thermal response of multiple cracks in a half-plane or along the interface between a thermal coating and a substrate by considering non-Fourier heat conduction. The comprehensive analysis reveals significant deviations from traditional Fourier models, highlighting the importance of thermal relaxation time, loading parameters, crack dimensions, and spacing in predicting the thermal response of cracked structures. Key findings demonstrate that the presence of thermal relaxation time results in dynamic overshooting, emphasizing the necessity for accurate modeling of transient thermal responses. This thesis provides a robust framework for analyzing the thermal and multiphysical behavior of cracked structures under various loading conditions. Inclusion of multiphysical effects under dynamic transient loading presents a more complex and challenging scenario for future studies. The insights gained are crucial for the design and optimization of thermal protection systems, particularly in extreme thermal environments, in order to enhance their reliability and performance. The integration of smart materials and advanced heat conduction models offers new perspectives for developing innovative solutions in material science and engineering

Drivers of Electric Vehicle Demand in the United States: A Random Coefficient Logit Model Approach

Abstract Research suggests that greenhouse gas (GHG) emissions contribute to global health and environmental degradation, mainly because of heavy reliance on fossil fuel-based transportation. Electric vehicles (EVs) have gained attention for their potential to reduce transportation-related GHG emissions. However, to fully understand the demand function of EVs, it is essential to examine the characteristics that make EVs attractive to customers. While much focus on increasing EV adoption has been placed on government incentives, understanding the specific characteristics of EVs that drive consumer adoption is equally important. Although the EV market has seen growth and some stability in recent years, what drives consumers to adopt EVs still needs to be explored. As EV adoption increases, it becomes critically important to understand the EV characteristics that influence consumer purchasing decisions. This study aims to employ a random coefficient logit model (BLP) to examine how EV characteristics, such as driving range, charging time, and price, influence consumer purchasing decisions and how consumer demographics, such as education attainment, place of residency, and income, affect EV consumer purchasing decisions. To achieve this research objective, we analyze county-level aggregate EV sales and demographic data in California from 2012 to 2022. Results suggest that income does not significantly impact price sensitivity. However, education level and urban residence influence consumer sensitivity to price changes. Specifically, the study found that highly educated consumers are more sensitive to price changes, while urban residents are less sensitive to price increases. Additionally, our findings reveal that income and education do not significantly influence preferences for increased EV driving range, whereas urban residents prefer increased driving range less than rural residents. Furthermore, results suggest that income and education do not influence preferences around charging times for EVs. Interestingly, urban residents are less sensitive to charging time than rural residents. Finally, our analysis suggests that preferences for EVs significantly depend on the EV characteristics, price, and consumer socio-demographic factors. Overall, this research highlights the importance of EV characteristics and price for EV adoption and provides valuable insights for policymakers and automakers in the EV industry

The Blurring of Imperfect Pieces

This image portrays a fragmented face embraced by the blurry backdrop of nature. The two sides of the face are joined together by a tree trunk weaving its way through the middle of the canvas, and creating an umbrella of leaves above the head. My research focuses on the ways in which trauma-sensitive practices support one’s journey towards wholeness. This art piece aims to capture the paradoxical relationship between the fragmented parts of self and the unified cycle of life to which each person belongs. The mosaic of jagged felt pieces against the blurred spirals of acrylic paint resists the ‘perfect portrait’ of self that capitalist societies often encourage individuals to pursue. Importantly, the eyes form the focal point of the image, symbolizing a key aspect of healing, which involves the willingness to see oneself and the vulnerability to be seen as imperfect

Questions pour un bibliothécaire

Cet article propose un récit biographique du point de vue de l’auteur sur ses responsabilités professionnelles actuelles au sein d’une bibliothèque universitaire, les motivations qui l’ont conduit à choisir la bibliothéconomie comme carrière, ses contributions aux activités de recherche dans son établissement, ainsi qu’une analyse des défis et des opportunités auxquels sont confrontés les bibliothécaires universitaires. This article presents a biographical account of the author’s perspective on his current professional responsibilities within an academic library, the motivations underlying his decision to pursue librarianship as a career, his contributions to research activities at his institution, and an analysis of the challenges and opportunities encountered by academic librarians

Sorted Bucket Hash and Combinatorial Game Algorithms: Two Efficient Solvers for the Game of NoGo

NoGo is a variant of the popular game of Go. NoGo shares the same mechanisms as Go, but it requires stones, once played, to be not removed from the board. Strong computer players have been created for NoGo, yet the game properties and optimal play strategies are not well studied. This thesis describes our recent contributions to solving and understanding NoGo. Two solvers were developed for this purpose: SBHSolver and CGTSolver. SBHSolver uses a newly proposed Sorted Bucket Hash hashing method and its general data structure to build the transposition table. It is capable of weakly solving games on memory-limited machines efficiently. CGTSolver equips Negamax search algorithm with enhancements derived from combinatorial game theory to solve Linear NoGo which is NoGo played on a one-dimensional board. SBHSolver weakly solved all NoGo positions of sizes up to 27 points, and CGTSolver pushed beyond and ultra-weakly solved Linear NoGo up to 39 points. Those achievements were made possible by the improved efficiency of these solvers. Statistical observations of game-playing strategies, rigorous proofs of combinatorial game properties, and detailed experimental results are provided in this thesis

Impacts of the Enhanced Train Control (ETC) System on Train Operators in Canadian Railways

Canada's rail transportation system is a vital part of the economy, ensuring the efficient movement of goods and passengers. However, maintaining safety and efficiency remains a challenge, as the system relies on human operators, making it susceptible to human errors. The Transportation Safety Board of Canada (TSB) reported an average of 35 missed signal incidents per year between 2004 and 2021, underscoring the associated risks. To enhance railway safety, the TSB and the 2018 Railway Safety Act Review Panel recommended implementing a train control system similar to Positive Train Control (PTC) in the U.S., the European Train Control System (ETCS), and the Chinese Train Control System (CTCS). Transport Canada is now developing a vision for Enhanced Train Control (ETC), though it is not yet federally regulated. A Notice of Intent in the Canada Gazette, Part I, outlines a risk-prioritization approach for ETC’s implementation. The introduction of train control systems, however, raises concerns about the potential for suboptimal workload levels for train operators, which could degrade both operator and system performance. As a result, mental workload assessments must be integrated into system design. While workload models have been widely applied in aviation and nuclear power fields, their use in railway operations — primarily through analytical methods —remains limited. Moreover, existing studies have mainly focused on static mental workload predictions, neglecting the dynamic cognitive demands that train operators face throughout a journey. This research explores vulnerabilities in train control systems by examining their impact on train operators' mental workload within Canadian Railways. To achieve this, it begins with a review of systematic accident analysis methods, such as the Human Factors Analysis and Classification System (HFACS) and Functional Resonance Analysis Method (FRAM), establishing a systemic approach to evaluating complex train control systems and gaining a deeper understanding of accident dynamics in railways. Subsequently, data on Passing a Stop Signal (PASS) occurrences in Canadian railways, along with weather and geospatial information, were collected, integrated, and analyzed using XGBoost and SHAP machine learning methods. A binary classification model was developed to predict PASS occurrences based on relevant features, while SHAP analysis identified and ranked key non-human contributing factors, assessing their collective impact on missed signals. The findings indicate that track geometry, environmental conditions, and operational factors significantly increase the risk of PASS occurrences in freight trains on Canadian mainlines. Specifically, sharp track curvatures before signals, downhill gradients, low temperatures, high humidity, and low atmospheric pressure were found to be major contributing factors. These elements interact in complex, non-linear ways, affecting signal visibility, stopping distances, and brake performance, ultimately increasing the likelihood of passing stop signals. Comparing the XGBoost model with Logistic Regression highlighted XGBoost’s superior ability to capture these intricate non-linear interactions, reinforcing its effectiveness in modelling complex dependencies. These insights guided the development of safety measures to mitigate PASS incidents and contributed to refining workload measurement through human performance shaping factors. The core of this thesis introduces a hybrid workload measurement method that integrates VACP analysis, fuzzy set theory, and SPAR-H to enhance the traditional VACP method and assess workload across various train control automation levels and real-world settings. Applied across Canadian rail routes under various train control systems and operational conditions, the model revealed that automation does not always reduce mental workload. Compared to highly automated systems, low and Intermediate levels of automation were found to increase workload, particularly when compounded by challenging track and weather conditions. Workload also varies along routes based on environmental, operational, and infrastructural factors; the lower the automation level, the more external conditions impact operators. Based on the findings, safety recommendations were provided, including human factors requirements, system integration, alarm management, and training programs. This research enhances our understanding of the complex relationship between train control systems, contextual factors, and train operators' mental workload. It introduces a novel analytical mental workload assessment methodology, establishes a baseline for evaluating ETC’s impact on operators, promotes a common framework among stakeholders, and expands the field's knowledge. These findings benefit safety professionals, policymakers, and regulators by supporting the development of evidence-based policies and proactive safety measures for train control implementation

The Hill Times, Monday, January 13, 2025

The newspaper of Parliament