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Recent Submissions
The role of narratives of care in sustainability
(University of Waterloo, 2025-09-18) Blanco Murcia, Laura
Current dominant narratives about human nature - those that portray humanity as inherently self-centered, extractive, and utilitarian - are incomplete and insufficient to foster transformations towards sustainable scenarios. Since these prioritize transactions and an extractive interaction with the human and more than-human-world, they are instead accelerating environmental degradation and deepening social inequalities. Narratives of care emerge as powerful counter-narratives, by providing holistic, relational, and inclusive solutions. Narratives of care have the potential to reshape relationships in social -ecological system, as these are grounded in empathy, recognition, and interdependence. This dissertation explores the role of narratives of care in sustainability, with a focus on sustainable food consumption.
This research explores how narratives of care can challenge dominant transactional paradigms and support the co-creation of alternative, relational-based scenarios, that offer wellbeing for humans and more-than-humans. Drawing on interdisciplinary perspectives - such as Narrative Psychology, Narrative Therapy, the Ethics of Care, and Complex Adaptive Systems theory - this dissertation proposes a framework for narrative transformation that is applied to individual and community narratives around food.
The first manuscript introduces a theoretical framework based on narrative therapy for tilting dominant self-centered narratives toward narratives of care and interdependence. The framework includes three main phases 1) identifying the dominant narrative and the assumptions that sustain it; 2) finding unique outcomes or events and experiences that contradict this dominant narrative, and creating alternative narratives of care based on these; and 3) reinforcing care-oriented scenarios. This framework is applied to the topic of consumption and proposes reframing sustainable consumption as an act of care rather than a sacrifice. The proposed shift allows for a deeper engagement with sustainable behaviors, by reframing them in moral and relational terms, as well as by appealing to our human capacity for empathy and responsibility.
In the second manuscript, the framework is applied to the individual food narratives of 22 Colombian adults to understand how the meaning of care linked to food can be expanded to foster sustainable food consumption. Narratives are approached through a qualitative methodology by using life story interviews and participant-created storybooks. This study revealed three alternative stories that question the transactional narratives that see food as a commodity and can aid in expanding the meaning of care to move towards sustainable food systems. These alternative narratives encompass reconnecting with emotions, finding commonalities, and stopping the transmission of suffering.
The third manuscript focuses on the communal level, exploring how care-based narratives are lived, negotiated and transmitted within Nashira, a female-led ecovillage in Colombia. A qualitative methodology - based on ethnography, interviews, and a communal narrative session – is employed to understand how previous narratives are questioned and transformed within a sustainable setting. This study reveals how the community shifts from individual narratives of exclusivity, privatization, and patriarchal hierarchies, to communal narratives of inclusion, sharing, and mutual empowerment. It also reveals the importance of embracing tensions as an essential part of community life and a source of creativity in problem solving.
Together, these three studies demonstrate that - while care can start by conversations questioning the status quo – it is mostly relational, practical, and must be enacted. Everyday acts of care – such as those surrounding food practices – materialize and visualize concerns, playing an essential role in nurturing alternative narratives. These findings contribute to the academic discussion around the role of narratives in sociocultural transformations towards sustainability. This research also offers a framework that can be applied both in academic and non-academic settings, to understand and promote socio-ecological transformations. At the policy level, this work suggests that supporting narratives and acts of care at the individual and community level, can be a vital complement to wider technical and economic sustainability strategies. By transforming the way in which we tell stories about ourselves, our food, and our communities, and by acting on our immediate context, we can shape more caring and sustainable futures.
Functionally Graded Additive Manufacturing of Inconel 625 and CuCrZr Alloys
(University of Waterloo, 2025-09-18) Zardoshtian, Ali
Significant advancements over the past decade have transformed metal additive manufacturing from a prototyping tool into a full-fledged production process. These developments have enabled the use of lighter, stronger, and more cost-effective additively manufactured components in aerospace, automotive, and energy industries. As qualification efforts progress, research is increasingly focused on advanced capabilities such as combining multiple alloys within a single build to create functionally graded structures, eliminating the need for additional joints. In that regard, Functionally Graded Additive Manufacturing (FGAM) is a layer-by-layer process that varies composition and/or microstructure within a component to achieve locally tailored properties. A new class of FGAMs combining highly heat-conductive CuCrZr alloy with Inconel 625 superalloy has gained considerable attention for aerospace applications, leveraging the former’s high heat dissipation and the latter’s excellent mechanical properties. This can be done through the Laser Directed Energy Deposition (L-DED) technique; however, the implementation remains a material-processing challenge due to the noticeable thermophysical mismatch between the two alloys.
This dissertation provides a comprehensive investigation into the FGAM of IN625-CuCrZr alloys, encircling process parameter optimization, gradient path development, and microstructural and defect formation analysis through advanced characterization, CALPHAD-based thermodynamic simulations, and finite element modeling. In that regard, process parameters have been optimized from single-track to multilayer scales, and the effect of process parameters on the microstructure has been studied, more specifically on CuCrZr alloy as there was a big gap in the literature.
Further, the FGAM of IN625-CuCrZr has been exercised for two geometries of thin wall and cuboid, incorporating both sharp and gradual compositional transitions. Sharp transitions led to delamination at the interface, while gradual transitions resulted in structurally sound builds. In the gradual transition zone, the presence of a metastable miscibility gap between the liquid of the two alloys led to the formation of distinct Cu-lean and Cu-rich phases in the microstructure, a phenomenon predicted through CALPHAD-based thermodynamic simulations. The formation of solidification cracking in the gradient region of the cuboid geometry was further investigated using Kou’s cracking susceptibility criterion.
In support of these findings, a multi-step numerical investigation of heat transfer in both thin wall and cuboid geometries was conducted using finite element analysis. First, a hybrid statistical–numerical thermal model was developed and implemented in the scale of single tracks through user-defined subroutines (DFLUX, USDFLD, and FILM) in Abaqus software. This model enabled high-fidelity prediction of melt pool geometry and thermal history and was validated against experimental melt pool dimensions and in-situ thermocouple measurements. Subsequently, the validated heat source model was used to simulate the thermal behavior during FGAM processing of both geometries. The thermal simulations highlighted the critical role of geometry on cooling rates and temperature distributions, providing deeper understanding into cracking behavior and how geometry-dependent thermal history influence microstructure and defect formation during FGAM of IN625-CuCrZr alloys.
Overall, this work establishes a robust experimental–computational framework for FGAM of dissimilar alloys using L-DED process. It introduces a scalable strategy for depositing functionally graded IN625–CuCrZr structures with controlled transitions and minimized defects. The modeling and characterization approaches developed here can be extended to other material systems, while the insights into miscibility gap, solidification behavior, and cracking mechanisms lay the groundwork for future microstructure design and process control in metal additive manufacturing.
Building Haptic Systems with 3C's: Communication, Collaboration, and Confirmation
(University of Waterloo, 2025-09-18) Joshi, Bibhushan Raj
Haptic technology is essential for bringing the sense of touch to digital and virtual environments, with applications ranging from accessibility to education and entertainment. However, the development of haptic systems is hindered by the absence of a standardized, human-centered process. This fragmentation leads to redundant efforts, and challenges in translating prototypes into reliable, user-ready products. This thesis addresses this gap by systematically investigating the haptic development lifecycle and introducing the 3C Guidelines, an integrated, evidence-based approach centered on three critical actions: Communication, Collaboration, and Confirmation.
To address the challenges in Communication, this research introduces and evaluates the Feel-Play-Imagine (FPI) process. Through a co-design workshop and a qualitative study with domain experts, the FPI process facilitates the communication of haptics projects through hands-on exploration to build a shared sensory literacy, and enabling stakeholders to effectively articulate and incorporate haptic concepts into their designs.
For Collaboration, this thesis presents findings from two distinct contexts: a four-year longitudinal case study on the co-design of an interface for a sensitive, community-driven narrative, and a participatory design workshop with haptics experts. Together, these studies revealed the critical importance of building restorative justice principles that fosters trust, synchronizes diverse work rhythms, and establishes shared ethical commitments, highlighting practices that support effective and inclusive teamwork in interdisciplinary haptic projects.
Finally, to understand Confirmation, this work includes a scoping review of 464 empirical studies, which mapped current evaluation practices in haptics research. The review identified significant gaps, including a reliance on ad-hoc metrics, a lack of demographic diversity in participant samples, and insufficient transparency in documentation. In response, this chapter provides a set of evidence-based recommendations to guide researchers toward more rigorous and reproducible evaluation methods.
By synthesizing the findings from these three investigations, this dissertation contributes a practical and actionable guideline designed to assist both novice and experienced practitioners in developing haptic systems more effectively. The 3C Guidelines provides structured yet flexible lens to improve how teams communicate, collaborate, and confirm their work, ultimately aiming to raise the quality, impact, and reproducibility of future haptic systems.
Multiphase Competition and Quantum Moment Fragmentation in Dipolar-Octupolar Pyrochlore Materials
(University of Waterloo, 2025-09-18) Howson, Griffin
In the field frustrated magnetism, magnetic pyrochlore oxides are one of the most widely explored platforms for realizing exotic phenomena in three dimensions, including classical, fragmented and quantum spin liquids.
In this thesis, we elaborate on the existing understanding of the low-temperature properties of a subclass of magnetic pyrochlore oxides -- the so-called dipolar-octupolar (DO) pyrochlores -- using various analytical and numerical techniques.
To begin, we explore the `XZ' line of the DO phase diagram, which exhibits a strong competition between neighbouring quantum spin liquid and long-range ordered phases using large-scale quantum Monte Carlo (QMC).
In particular, we examine how this competition can explain the low-temperature behaviours observed in moment fragmentation candidate Nd_2Zr_2O_7.
We proceed by examining the thermodynamics and correlations exhibited by another DO pyrochlore Ce_2Sn_2O_7, arguing that it too harbours moment fragmentation in the T=0 ordered state by comparing and contrasting its behaviour with Nd_2Zr_2O_7.
To further elaborate the extent of this phenomenon, we scan the XZ line discussing the evolution of fragmentation as one varies the microscopic exchange parameters between neighbouring ordered and disordered phases in the DO phase diagram.
Extending beyond the nearest-neighbour (NN) model, we present a derivation of the symmetry-allowed exchange interactions for the DO model up to third nearest-neighbours.
Taking a subset of these new exchange parameters, we construct a mean-field phase diagram centered at the NN exchange parameters presented for quantum spin ice candidate Ce_2Zr_2O_7.
In particular, we identify the ordering wavevectors characterizing the long-range order stabilized by lifting the degeneracy exhibited by the NNx model.
Further, we present estimates of the critical temperatures associated with the ordering transitions presented, defending the lack of order observed in experiments.
Computationally Efficient Multi-Model Adaptive Control and Estimation for Uncertain Systems
(University of Waterloo, 2025-09-18) Mafi Shourestani, Farid
Multi-model adaptive technique offers a powerful framework for handling uncertainties in dynamic systems by employing multiple models that represent different operating conditions. However, despite strong theoretical foundations, practical deployment has been severely limited by the curse of dimensionality—the exponential growth in computational requirements as system complexity increases, creating a major obstacle for real-time implementation. This thesis presents a comprehensive framework to address this computational challenge through three related contributions, achieving dramatic reductions in complexity while maintaining or improving system performance.
The main insight is that traditional multi-model approaches use far more models than necessary. Instead of using every possible model combination to cover parameter uncertainties, this thesis shows that carefully chosen subsets can achieve the same coverage with much less computation. This shift from using all models to selecting the right models makes multi-model control practical for real-time systems with limited resources.
To this end, geometric methods have been developed that analyze how models cover the space of possible system behaviors. Using computational geometry principles, new Enclosed Polytope with Minimum Models (EPMM) algorithms find the smallest set of models that still covers the uncertainty space adequately. These algorithms work like placing sensors strategically—finding the fewest locations needed to monitor an entire area. An optimization framework extends this idea to continuous parameter spaces, while a transfer function method handles high-dimensional systems efficiently by focusing on input-output relationships rather than full state representations.
To overcome the fundamental scaling limitations in high-dimensional spaces, the Parameter -Tying Theorem has been developed as a theoretical innovation showing that changing to the right coordinates can considerably simplify high-dimensional uncertainty spaces. By analyzing systems in controllable canonical form and finding monotonicity properties in how parameters affect the system, the theorem proves that the number of required models can drop from exponential in the number of parameters to potentially constant. The framework extends through five conditions—including affine relationships, symmetry, and coordinated parameter variations—making it applicable beyond strictly monotonic systems.
Furthermore, a unified estimation framework has been designed to tackle the integration of physics-based and data-driven models. A consensus multi-model Kalman filter combines different model types based on how well they perform. Two methods enable proper uncertainty handling: Koopman operator-based linearization allows analytical covariance propagation for neural networks and other nonlinear data-driven models, while an ensemble-based approach provides model-independent uncertainty quantification without needing offline training. The consensus fusion automatically adjusts model weights based on prediction errors, ensuring smooth transitions between models as conditions change. Extensive experimental data from an electric all-wheel-drive vehicle under extreme conditions shows major performance improvements over traditional single-model approaches.
This thesis transforms multi-model approach from an attractive theory with limited practical use into a viable solution for real-world applications. By combining geometric insight, coordinate transformation theory, and heterogeneous model integration, it addresses the fundamental implementation barriers. The developed frameworks maintain mathematical rigor while achieving the computational efficiency needed by modern embedded systems. These advances enable robust adaptive control across diverse operating conditions, with immediate applications in autonomous vehicles, renewable energy systems, and other areas where handling uncertainty is critical. The principles established here provide a foundation for addressing high-dimensional uncertainty in complex dynamical systems across engineering fields.