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Role of Social Capital and Relational Well-being in Shaping the Community Level Responses to Tropical Cyclones among the Small-Scale Fisheries Communities in Chilika Lagoon, India
No full textSmall scale fisheries (SSFs) are more vulnerable to calamities brought on by natural hazards, changing climatic conditions, and climate change due to their proximity to the seashore. Dealing with these challenges is an added burden to already existing vulnerabilities, injustice and marginalization faced by them. The Indian subcontinent with a vast coastline extending up to 7516 kms (about 4670.23 mi), is vulnerable to world’s 10% tropical cyclones, especially in the places adjacent to Bay of Bengal (BoB). Asia’s largest and world’s second largest brackish water lagoon, adjacent to BoB - Chilika lagoon, situated in Odisha state of India is extremely prone to catastrophic events, causing around 5-6 cyclones hitting the coast annually. SSFs who depend on the lagoon for their livelihood are on the forefront suffering from the repercussions of cyclonic activities. While resilience against events like cyclones is usually analyzed in terms of economic and infrastructure aspects, there is a lack of focus on the intrinsic material aspects contributing to community resilience in the face of climate related disasters. This research fills this gap by analyzing the community resilience of SSF’s in Chilika Lagoon through the lens of social capital and relational well-being. Social capital measures the different links or connections a community has within and outside of their network that helps them build effective response strategies through collective action at the time of crisis. Communities with high social capital can bring community members together for better preparedness, emergency support, response, and recovery efforts. Nevertheless, it is not the existence of all these linkages that matters, but the quality and balance of all these ties are imperative. For instance, the effectiveness of these could be hindered in a community level resilience if it lacks the ability to address the power imbalance, social inequality, and trust.
Thus, relational well-being measures the quality of various networks through characteristics such as trust, reciprocity, support, and network dynamics which create a sense of motivation to work collectively. The study employs a qualitative case study approach and multiple data collection tools such as semi-structured interviews, non-participant observations, and focus group discussions. The key findings present the various challenges faced by the communities in various systems like social, economic, environmental and physical and their interconnectedness, role of social capital and relational well-being in the various community level response to deal with the crises, the lack thereof due to power imbalance, social inequality, caste system and political power and finally providing recommendations to ensure tailored context specific approaches to enhance the community resilience against disasters like tropical cyclone in the future
Mixed Triangular-Square Lattices on a Spherical Surface
No full textDefects play a crucial role in determining the structures and properties of materials. When putting lattices onto a curved surface that has non-trivial Euler characteristics, defects must appear due to the geometric frustrations. Extensive studies have shown the singular lattices on curved surfaces presenting point- and scar- disclinations with high symmetry. Of recent interest is the mixed ordered lattices on flat surfaces. The phase separation of multi-ordered lattices produces a complicated, maze-like structure, where defect also plays an important role. It makes us curious about the defect properties when arranging mixed lattices on a curved geometry. In the thesis, we propose and discuss a computational and theoretical approach to study the properties of the mixed two-dimensional triangular-square lattices on a spherical surface.
First, we introduce Hertzian interactions in molecular dynamics simulations to stabilize the coexistence of triangular and square domains and enable soft particles to self-assemble on a sphere. The simulations reveal novel defect morphologies beyond conventional point and scar defects—domains of one lattice type acting as defects within the bulk of the other, arranged with unexpected symmetry.
To analyze these assemblies further, we develop tiling methods to arrange mixed triangular-square lattices onto spherical geometries, where defects type and locations are determined. we generalize the Caspar–Klug construction for triangulations of the sphere and introduce two operations---Face‐Rotation (FR) and Cut‐and-Rotate (CR)---to generate mixed tilings with minimal defects and high symmetries.
This tiling methods enables the comparison of the defective energies of these structures with a coarse-grained model developed by Bowick \textit{et al.}. A state diagram is plotted to show the energetically favored structure at a given area fraction of square lattices. Finally, we compare these mixed configurations to singular lattices via a limiting approximation to their energy landscapes.
Our work presents a new angle to understand the tripartite tug-of-war among crystalline orders, defects, and topology—an interplay that occurs across scales, from biomolecular assemblies to architectural frameworks
Laser powder bed fusion of Cu alloys: from process parameter optimization to oxidation analysis
No full textThis dissertation investigates two Cu-based alloy systems, Cu–Cr–Zr and Cu–Ag, with distinct research focuses for each alloy. The first part focuses on Cu–Cr–Zr, aiming to optimize laser powder bed fusion (LPBF) processing conditions and evaluate mechanical performances of the alloy under high strain rate loading. The second part focuses on Cu–Ag, with the goal of understanding its oxidation behavior at high temperatures, particularly when processed by LPBF. Through a systematic approach, this work establishes direct links between processing parameters, microstructural evolution, and functional properties, providing critical insights into tailoring these alloys for advanced structural and functional applications.
In the first part, a comprehensive multi-stage statistical optimization is applied to identify the optimal LPBF process parameters to maximize relative density and minimize surface roughness of Cu–Cr–Zr. A Plackett-Burman design (PBD) is first employed to screen 23 process parameters and identify the most influential factors. The key parameters, including laser power, scanning speed, hatch spacing, and layer thickness, are then fine-tuned using a central composite design (CCD) to develop predictive models for both relative density and surface roughness. The optimized process achieves near-full densification, with relative density exceeding 99.5％ and surface roughness below 15 μm.
To evaluate dynamic mechanical behavior, the optimized Cu–Cr–Zr samples are subjected to high strain rate loading using a split Hopkinson pressure bar (SHPB), with strain rates ranging from 4400 1/s to 11300 1/s. The alloy demonstrates significant strain hardening, followed by thermal softening and adiabatic shear band (ASB) formation. At the highest strain rate of 11300 1/s, the flow stress exceeds 450 MPa, while the alloy maintains considerable strain accommodation due to the dynamic activation of slip systems and localized deformation within ASBs.
In the second part, the feasibility of in-situ alloying of Cu–Ag using LPBF is systematically investigated. Single-track experiments show that increasing scanning speed reduces melt pool size, limiting Ag dissolution, while higher laser power promotes homogeneity at the cost of increased keyhole porosity. Through process optimization, the study achieves a high relative density exceeding 99.2％, along with a uniform distribution of Ag throughout the matrix.
The high-temperature oxidation behavior of optimized thin-walled and triply periodic minimal surface (TPMS) Cu–Ag components is then examined across temperatures ranging from 300 ℃ to 800 ℃. Thermogravimetric analysis (TGA) reveals a clear transition from sub-parabolic to parabolic oxidation behavior as temperature increases. At lower temperatures (300 ℃ to 600 ℃), Cu–2Ag initially oxidizes faster than pure Cu; however, its oxidation rate decreases significantly over time, ultimately resulting in lower total mass gain than pure Cu. At higher temperatures (700 ℃ to 800 ℃), Cu–2Ag exhibits superior oxidation resistance from the outset, with a slower and more stable oxidation rate throughout the exposure period. The presence of Ag shifts the oxidation mechanism toward a more protective parabolic regime, indicating the formation of a stable, adherent, and refined oxide scale. Together, these findings confirm the viability of in-situ alloying via LPBF and highlight the potential of Cu–Ag alloys for high-performance applications, particularly in environments where thermal stability and oxidation resistance are essential
Simulated spin qubits in silicon quantum dots and enhancement of InGaAs photodetectors
No full textSemiconductor quantum dot spin qubits are a leading candidate for scalable, fault-
tolerant quantum computing. Their advantages include nanoscopic device size, compat-
ibility with foundry fabrication processes, and long coherence times relative to gate du-
rations. The fabrication and control of a quantum processing unit composed of tens of
thousands to millions of physical qubits pose many engineering challenges. These chal-
lenges fall broadly into two categories: device design, such as optimizing the geometry for
high-quality qubit formation, and qubit control, which involves the precise manipulation
of spin or charge states in qubits that are capacitively coupled to numerous neighboring
electrodes. In this thesis, we develop a simulation tool that accelerates device design iter-
ation prior to fabrication by providing a priori knowledge of the quantum dot electrostatic
potential landscape as a function of external electrode voltages. This enables effective
spin and Hubbard Hamiltonian parameters to be computed before experimental charac-
terization, facilitating early-stage control method development and device performance
prediction. The tool, implemented as the Python-based QuDiPy package, integrates three-
dimensional finite-element Poisson solutions with modules for electrostatic reconstruction,
Hamiltonian parameter extraction, and control pulse optimization. Unlike previous dis-
jointed toolchains, QuDiPy offers a unified workflow for full-stack qubit control simulations,
including automated voltage-to-Hamiltonian mapping for exploring high-dimensional gate
voltage spaces and mitigating crosstalk in dense qubit arrays. The simulator is designed
to be memory- and CPU-efficient to enable computationally efficient simulation of linear
quantum dot arrays consisting of several qubits. Simulation of small quantum dot arrays
serves as a design tool for control protocols within multi-node quantum processors. Sim-
ulation of spin qubit dynamics in many-qubit nodes connected in a network enables the
study of required voltage ranges for maintaining stable charge configurations in the device.
It also supports the design of experimental input pulses to generate maximally entangled
Greenberger–Horne–Zeilinger (GHZ) states between nodes, a key step for implementing
surface code error correction protocols.
Spin qubit control requires a precise understanding of the impact of experimental con-
trols, such as electrode voltages or radio-frequency magnetic field amplitude and phase,
on effective parameters such as electronic g-factor, exchange energy, chemical potential,
etc. A mapping between experimental and effective parameters is created by performing
effective parameter calculations on two-dimensional cross-sections of the electrostatic po-
tential landscape obtained from a 3-dimensional Poisson solver nextnano++, a commercially
available, 3D Poisson solver chosen for its robustness, flexibility in defining quantum device
geometries, and proven accuracy in modeling semiconductor heterostructures at cryogenic
temperatures. First, a 2D cross-section of the electrostatic potential landscape is taken
along the growth direction of the quantum dot device, near the heterojunction where qubit
formation occurs. This region is selected because it captures the horizontal confinement
profile most relevant to charge localization and wavefunction shape. The cross-section is
extracted for all simulated voltage configurations applied to the gate electrodes. Second,
the single-particle ground state or first excited state wavefunctions are determined using
a non-uniform grid Schrödinger solver for all voltage configurations and for each isolated
quantum dot or nearest-neighbor quantum dot pair. The non-uniform grid provides higher
spatial resolution near confinement potential minima, enabling more accurate modeling
of localized wavefunctions where precision is most critical. The mapping between input
voltage and single-particle wavefunctions is leveraged, along with numerical integration
routines, to calculate the desired effective parameters as a function of voltage. The chemi-
cal potential, tunnel coupling, and onsite and interdot Coulomb parameters are computed
for each voltage configuration. This enables exact diagonalization of the Hubbard Hamil-
tonian at every point in voltage space and identifies the regions of charge stability for a
multiqubit quantum dot device. This step is essential for establishing control over the
quantum processor.
The second part of this thesis investigates optoelectronic device enhancement using
localized surface plasmons in nanocrystals. Fast and accurate detection of light in the
near-infrared (NIR) spectral range plays a crucial role in alleviating speed and capacity
bottlenecks in optical communications and in enhancing the control and safety of au-
tonomous vehicles through NIR imaging systems. Several technological platforms are cur-
rently under investigation to improve NIR photodetection, aiming to surpass the perfor-
mance of established III–V semiconductor p-i-n (PIN) junction technology. These plat-
forms include in situ-grown inorganic nanocrystals (NCs) and nanowire arrays, as well as
hybrid organic–inorganic materials such as graphene-perovskite heterostructures. How-
ever, challenges remain in NC and nanowire growth, large-area fabrication of high-quality
2D materials, and the fabrication of devices for practical applications. Here, we ex-
plore the potential for tailored semiconductor NCs to enhance the responsivity of planar
metal–semiconductor–metal (MSM) photodetectors. MSM technology offers ease of fabri-
cation and fast response times compared to PIN detectors. We observe enhancement of the
optical-to-electric conversion efficiency by up to a factor of ∼2.5 through the application
of plasmonically-active semiconductor nanorods and NCs. We present a protocol for syn-
thesizing and rapidly testing the performance of non-stoichiometric tungsten oxide (WO)
nanorods and cesium-doped tungsten oxide (CsyWO) hexagonal nanoprisms prepared in
colloidal suspensions and drop-cast onto photodetector surfaces. The results demonstrate
the potential for a cost-effective and scalable method exploiting tailored NCs to improve the performance of NIR optoelectronic devices
Exploring Food Insecurity as a Risk Factor for Eight Enteric Infections in Ontario, Canada, 2019-2022: Multilevel Ecological Study
No full textFood insecurity and foodborne illness are significant public health issues in Canada, associated with poor health, direct healthcare costs, and lost productivity. While previous ecological studies in higher-resource countries have explored the relationship between broad socioeconomic determinants and foodborne infections, none have explored food insecurity as a risk factor for enteric infections. Food insecurity is recognized as a potential intermediary determinant that can influence an individual’s vulnerability and exposure to foodborne illness. This study aimed to identify the magnitude, distribution, and spatial patterns of reported Campylobacter spp., Salmonella spp., Shiga toxin-producing E. coli (STEC), Listeria monocytogenes., Shigella spp.,
Cyclospora cayetanensis, Giardia duodenalis spp., and hepatitis A virus infections across Ontario, Canada, from January 2019 to December 2022. Additionally, it explored whether age- and sex-adjusted annual pathogen-specific incidence rates were associated with the prevalence of
household food insecurity at the Public Health Unit (PHU) level during 2021.
Public Health Ontario’s publicly available surveillance tools were used to collect data on household food insecurity and reported case counts of eight enteric infections for 34 Ontario PHUs. Annual age- and sex-adjusted pathogen-specific incidence rates were calculated for each PHU using direct standardization and were visualized using choropleth maps. Purely spatial,
temporal, and space-time high infection rate clusters were identified using retrospective scan statistics, with a Poisson model. Global and local spatial autocorrelation patterns of annual pathogen-specific incidence rates were examined using the Moran’s I spatial statistical method. Fixed and random effects Poisson and Negative Binomial regression analyses were conducted to estimate incidence rate ratios (IRR) for each enteric pathogen and the prevalence of household food insecurity, while controlling for demographic and socioeconomic covariates. A geographically weighted Poisson regression analysis was used to explore whether the association between pathogen-specific incidence rates and household food insecurity differed spatially across PHUs.
Reported cases of all enteric pathogens, except for Listeria monocytogenes, had a noticeable decline after 2019. Campylobacter spp., Salmonella spp., and Giardia duodenalis consistently had the highest incidence rates across PHUs from 2019-2022. Spatial and spacetime analyses showed that Salmonella spp. and STEC high-infection rates mainly clustered in
Central- and South-West regions of Ontario. Campylobacter spp., Listeria monocytogenes, Shigella spp., Cyclospora cayetanensis, Giardia duodenalis, and hepatitis A infections were widely distributed across the province. Campylobacter spp., STEC, Listeria monocytogenes, and Cyclospora cayetanensis were the only pathogens to exhibit a temporal pattern, with infections clustering in the warmer months. Two significant space-time clusters of Salmonella spp. and hepatitis A were associated with confirmed outbreaks in Lambton Public Health and Middlesex London Health Unit. In 2021, the prevalence household food insecurity had an inverse
association with the incidence rates of Campylobacter spp., STEC, and Giardia duodenalis, with minimal spatial variability of the IRRs across PHUs. No significant associations were observed between household food insecurity and the other enteric pathogens.
Food insecurity may influence the incidence of foodborne illness at an aggregate level. Spatial and temporal clustering of enteric pathogens suggests local and seasonal risk factors could be associated with foodborne illness. Future research should investigate whether the
incidence rates of foodborne illness differ among populations experiencing marginal, moderate, and severe levels of food insecurity. The findings from this study could help PHUs develop public health interventions that simultaneously address food insecurity and food safety
Wandering / Sanctuary: Perceiving Learning Spaces Through A Tamil Sangam Lens
No full textThe role that architecture could play in shaping our world and hence our lives is undermined - and the role that it already plays in ingraining values promoted in a society is skimmed-over. For the complexity of any given society is a culmination of various societal, political and cultural values fueling it. Architecture then, becomes a by-product of these said values - thereby playing considerably the most consistent role in influencing the masses. A niche of this framework that this work focuses on is education and school architecture. To understand how school architecture could influence students’ education, it becomes essential that a good understanding of the educational values of a society is required. This is done by taking a critical look at the current educational values of our society.
The Sangam literature is a body of Tamil literature belonging to the Sangam Age (circa. 300 BCE - 300 CE) - a period of history in the southern part of India and parts of Sri Lanka. This body of work is a compilation of poems that deal with various aspects of life - philosophy, governance, ethics and many love poems that give vivid descriptions of the lives of these poets and their surroundings. Through analytical research of existing publications and some original works, the people of the Sangam Age are traced through these poetry to have had an epistemological approach to life, and their knowledge being deeply rooted to their context - where nature herself was considered The Greatest Teacher. These poems and the philosophy they embody are adopted as spiritual and cultural mentors and guides to get an understanding of how education and its context coalesce to form a school.
To embody the values of the Sangam texts, the city of Vellore in Tamil Nadu, India is chosen as the context to synthesise this hypothesis. The Vellore fort has in its unmaintained peripheries beyond its moat, pockets of memories from my childhood, and plots of land that are now dilapidated parks. Looking through an architectural lens, such a culturally significant “plot” deserves regulation and a typology of architecture that would propel the place’s heritage - while potentially enriching the culture of the city and its people.
This Master of Architecture thesis proposes an institutional complex in Periyar Poonga (one of the dilapitated parks inside the Vellore Fort complex), while trying to answer this question:
How can the spatiality of a pedagogy contribute to culturally enriching it
Elucidating the Effect of Sintering Time on the Process-Structure-Property Relationship of Inconel 625 Produced by Metal Extrusion Additive Manufacturing
No full textInconel 625 (IN625), a Ni-based superalloy valued for its strength and corrosion resistance, is known to suffer from microcracking during fusion-based additive manufacturing. Metal Extrusion Additive Manufacturing (MEAM) offers a solid-state alternative that eliminates solidification, thereby reducing the risk of microcracking. However, there is currently a lack of understanding between the interrelationship of process conditions, microstructure, and mechanical properties for IN625 produced by MEAM.
This study investigates the influence of sintering time, 5 mins (short time) versus 4 hrs (long time) at 1290 °C, on the densification, microstructure, and mechanical performance of MEAM-processed IN 625. The 4 hrs sintered sample achieved a higher relative density (99.5%) compared to the 5 mins sample (98.5%), and both developed (Nb+Mo) rich carbides with distinct morphologies and volume fractions. Extended sintering reduced residual porosity, resulting in improved tensile strength and elongation. Fractographic analysis confirmed ductile failure via microvoid coalescence in both cases. These findings underscore the critical role of sintering duration in optimising density; despite developing microstructure characteristics that should degrade mechanical properties at longer sintering times, the mechanical properties of the 4 hrs sample were superior to those of the 5 min sample, revealing reduction of porosity to be the critical mechanism for maximising mechanical properties for this alloy and process
Heterogeneity and homophily in coupled behavior-disease dynamics: from model structure to early warnings
No full textUnderstanding how human behavior and infectious disease dynamics interact is essential for anticipating and mitigating outbreaks. While coupled behavior-disease models have provided valuable insights into the feedback between disease transmission and vaccination behavior, many assume homogeneous populations and neglect the influence of social structure in shaping individual vaccination strategies. Traditional surveillance systems often lack timely data on vaccination behavior, making it difficult to monitor changes in public vaccine sentiment. Moreover, existing statistical methods for detecting early warning signals of critical transitions rely on assumptions that do not always hold in real-world settings. This thesis addresses these limitations by incorporating population heterogeneity and homophily into a coupled behavior-disease model, and by using the resulting simulations to support the training of data-driven models for forecasting outbreak risks from high-frequency social media data. Specifically, we develop a coupled behavior-disease model that distinguishes social media users from non-users, capturing indirect heterogeneity in how individuals access vaccine-related information. The model demonstrates that homophily slows the spread of pro-vaccine strategies, pushing the population closer to tipping points. It also suggests that early vaccine-related online discussions may offer predictive signals of future outbreaks. Building on these findings, we generate synthetic time series with heavy-tailed noise to mimic real-world social media data. These model-generated data are used to train deep learning classifiers, under CNN-LSTM and ResNet architectures, to detect early warning signals in social media data. These classifiers outperform conventional statistical indicators, such as variance and lag-1 autocorrelation, in both sensitivity and specificity. Finally, we extend the modeling framework to a generalized multi-group vaccination game, considering direct heterogeneity in levels of vaccine support. Simulations reveal that homophily contributes to the persistence of opinion polarization in the population, regardless of the presence of diseases. Together, these studies highlight the need to account for heterogeneity in modeling vaccination behavior and that homophily can have various effects depending on the states of the system. We also show that combining mechanistic models and data-driven techniques can help detect emerging risks of disease outbreaks, informing more proactive public health policies
Design and Implementation of a Robust State of Charge Estimation Approach for a Single Battery Cell, a Hardware-in-the-Loop Test Bench, and a Battery Disconnect Unit for an Electric Vehicle Battery Pack
No full textAs transportation electrification accelerates, battery-powered vehicles, including cars, airplanes, and boats, are rapidly emerging. This thesis provides solutions and practical insights on two key topics: implementing robust machine learning algorithms on commercial Battery Management System (BMS), and building a high-performance Battery Disconnect Unit (BDU). The experience was gained during participation in the North American Battery Workforce Challenge.
First, two machine learning approaches for State of Charge (SoC) estimation are introduced. The first approach is an adaptive algorithm using SoC-OCV-T (State of Charge-Open Circuit Voltage-Temperature) lookup table and Extreme Learning Machine (ELM). The experiment began at 100% SoC, with temperature ranging from -20°C to 60°C. From -20°C to 0°C, the maximum absolute error (MAE) ranged from 0.030 to 0.025. In the mid-range from 5°C to 40°C, the MAE decreased to within 0.015 to 0.020 range. Lastly, at higher temperature range of 45°C to 60°C, the MAE was below 0.013.
In the second approach, advanced differential features are added to improve the accuracy of the ELM model, particularly below 0°C. Under noisy condition, both the maximum absolute error (MAE) and the root mean square error (RMSE) were reduced to below 1.5% at -20, 20, and 60°C. Both algorithms were validated on a customized Hardware-in-the-loop (HIL) test bench. The HIL platform was developed to streamline validation of algorithms such as SoC estimation.
Finally, the thesis details the design and testing process for the BDU, highlighting key design considerations, test results, and engineering challenges
Quantum Data Processing Inequalities and their Reverse
No full textAny reasonable measure of quantum information must satisfy a data processing inequality, that is, it must not increase under the action of a quantum channel. The same is, therefore, true for measures of distinguishability of quantum states. In this thesis, we study two families of distinguishability measures that are particularly interesting: the Riemannian metric (more precisely, the corresponding semi-norm) and the standard quantum f-divergences (sometimes referred to as just standard f-divergences). However, rather than focusing on the information lost, we ask about the information preserved - namely, a reverse data processing inequality. As is established in this thesis, an exact reverse data processing inequality for all states acted on by a specific channel is not possible for these measures if the output dimension of the quantum channel is no greater than the input dimension (which includes several important channels). Instead, we settle for a reverse data processing inequality on a restricted set of input states, or oftentimes it suffices to only compare the loss of information incurred via two given quantum channels in general. This thesis demonstrates cases of a restricted reverse data processing inequality for these measures and initiates a study of the similarities between the Riemannian metrics and standard quantum f-divergences in this context