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Livelihood Sustainability in the Age of Digital Platforms: Insights from Online Freelancing in Kenya
No full textDigital platforms are reshaping how online freelance work is organized and experienced in the Global South, but their implications for sustaining a meaningful and decent livelihood remain inadequately understood. While platforms offer new ways of making a living, particularly in labour markets characterized by informality and limited work opportunities, they often fail to meet the normative benchmarks typically associated with good-quality employment. These tensions raise questions about what constitutes a sustainable livelihood in the context of rapidly changing platform work, where conventional markers of decent work are increasingly difficult to apply to fragmented digital labour. My PhD research focuses on the intersection of platform work quality and development in the Global South. At its core, it explores the complexity of sustaining a fulfilling and good-quality livelihood through digital platforms, with particular attention to asymmetries in experienced work quality, local development realities, and the diverse, subjective aspirations of platform workers. Building on this foundation, my research bridges the knowledge gap by analyzing both workers’ subjective experiences of work quality, framed in relation to decent and meaningful work, and the resilience of their livelihoods to labour market disruptions, such as those driven by artificial intelligence (AI). Grounded in a mixed-methods case study of 52 Kenyan platform workers, this dissertation is structured as a collection of four interrelated research papers. The first paper is a systematic literature review that consolidates the fragmented empirical literature on platform work in the Global South, examining the quality of work, the complexities of skills development, and future career prospects. It then distills the insights into an analytical framework, offering a tool for analyzing how local contextual factors shape platform-labour outcomes. The second paper examines workers’ perceptions of work quality in relation to decent work standards, using a development lens. The findings of this paper situate platform work outcomes along a continuum from opportunity to precarity. The third paper focuses on how experiencing meaningful work evolves within the platform work economy. It discusses why many platform workers in the Global South perceive seemingly menial platform tasks as meaningful and analyzes the socioeconomic dynamics that shape this perception. The fourth paper examines the impact of generative AI on platform work. The paper introduces a task-based model to assess the impact of AI on work, offering an evaluation of four freelance occupations and an early-stage empirical validation of the model. Overall, this research advances theoretical debates on labour informality, decent and meaningful work, and automation risks to sustainable livelihoods by framing platform work as a complex livelihood strategy that simultaneously extends and disrupts traditional pathways of work. Empirically, the dissertation documents persistent inequalities and precarious conditions while highlighting developmentally important forms of opportunity access, worker autonomy, and worker resilience in Kenya’s platform economy. The dissertation offers policy-relevant insights, underscoring the need for adaptive labour regulation to extend protections to freelance platform workers, reorienting social safety nets to meet evolving demands, and inclusive digital economy governance so that platform-based work contributes to equitable and sustainable development
Optimal product replacement: The dark sides of technological progress and the circular economy
No full textThe final publication is available at Elsevier via https://doi.org/10.1016/j.resconrec.2025.108341. © 2025. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/The intersection of the circular economy and technological obsolescence is pivotal for understanding sustainable consumption and innovation's impacts on product lifecycle management. Prevailing literature emphasizes producer-driven obsolescence and often overlooks the consumer's role in product replacement designs, particularly in a circular economic context. This study bridges this gap by exploring consumer-driven economic obsolescence and the resulting implications for product longevity and replacement strategies. Employing a composite methodology that integrates economic modeling with net present value analysis, this article investigates the timing of optimal product replacement from the consumer perspective. It challenges the conventional minimum cost per unit time methods by introducing a profit-equating replacement strategy tailored to circular economy principles. Key findings reveal a paradoxical dynamic wherein increased technological advancement can inversely affect optimal replacement timing, underscoring a consumer-centric view of obsolescence. Conclusively, this research refines our understanding of obsolescence by reconstructing its economic rationale within the circular economy, thus offering nuanced insights for sustainable product lifecycle management and policy-making
Precise and Scalable Constraint-Based Type Inference for Incomplete Java Code Snippets in the Age of Large Language Models
No full textOnline code snippets are prevalent and are useful for developers. These snippets are commonly shared on websites such as Stack Overflow to illustrate programming concepts. However, these code snippets are frequently incomplete. In Java code snippets, type references are typically expressed using simple names, which can be ambiguous. Identifying the exact types used requires fully qualified names typically provided in import statements. Despite their importance, such import statements are only available in 6.88% of Java code snippets on Stack Overflow. To address this challenge, this thesis explores constraint-based type inference to recover missing type information. It also proposes a dataset for evaluating the performance of type inference techniques on Java code snippets, particularly large language models (LLMs). In addition, the scalability of the initial inference technique is improved to enhance applicability in real-world scenarios.
The first study introduces SnR, a constraint-based type inference technique to automatically infer the exact type used in code snippets and the libraries containing the inferred types, to compile and therefore reuse the code snippets. Initially, SnR builds a knowledge base of APIs, i.e., various facts about the available APIs, from a corpus of Java libraries. Given a code snippet with missing import statements, SnR automatically extracts typing constraints from the snippet, solves the constraints against the knowledge base, and returns a set of APIs that satisfies the constraints to be imported into the snippet. When evaluated on the StatType-SO benchmark suite, which includes 267 Stack Overflow code snippets, SnR significantly outperforms the state-of-the-art tool Coster. SnR correctly infers 91.0% of the import statements, which makes 73.8% of the snippets compilable, compared to Coster’s 36.0% and 9.0%, respectively.
The second study evaluates type inference techniques, particularly of LLMs. Although LLMs demonstrate strong performance on the StatType-SO benchmark, the dataset has been publicly available on GitHub since 2017. If LLMs were trained on StatType-SO, then their performance may not reflect how the model would perform on novel, real-world code, but rather result from recalling examples seen during training. To address this, this thesis introduces ThaliaType, a new, previously unreleased dataset containing 300 Java code snippets. Results reveal that LLMs exhibit a significant drop in performance when generalizing to unseen code snippets, with up to 59% decrease in precision and up to 72% decrease in recall. To further investigate the limitations of LLMs in understanding the execution semantics of the code, semantic-preserving code transformations were developed. Analysis showed that LLMs performed significantly worse on code snippets that are syntactically different but semantically equivalent. Experiments suggest that the strong performance of LLMs in prior evaluations was likely influenced by data leakage in the benchmarks, rather than a genuine understanding of the semantics of code snippets.
The third study enhances the scalability of constraint-based type inference by introducing Scitix. Constraint-solving becomes computationally expensive using a large knowledge base in the presence of unknown types (e.g. user-defined types) in code snippets. To improve scalability, Scitix represents certain unknown types as Any, ignoring such types during constraint solving. Then an iterative constraint-solving approach saves on computation and skips constraints involving unknown types. Extensive evaluations show that the insights improve both performance and scalability compared to SnR. Specifically, Scitix achieves F1-scores of 96.6% and 88.7% on StatType-SO and ThaliaType, respectively, using a large knowledge base of over 3,000 jars. In contrast, SnR consistently times out, yielding F1-scores close to 0%. Even with the smallest knowledge base, where SnR does not time out, Scitix reduces the number of errors by 79% and 37% compared to SnR. Furthermore, even with the largest knowledge base, Scitix reduces error rates by 20% and 78% compared to state-of-the-art LLMs.
This thesis demonstrates the use of constraint-based type inference for Java code snippets. The proposed approach is evaluated through a comprehensive analysis that contextualizes its performance in the current landscape dominated by LLMs. The ensuing system, Scitix, is both precise and scalable, enhancing the reusability of Java code snippets
Controlling Light with Photon Subtraction via the Single-Photon Raman Interaction
No full textThis dissertation leverages deterministic photon subtraction based on the single-photon Raman interaction (SPRINT) to engineer multiphoton quantum fields and design quantum optical platforms for applications ranging from non-Gaussian quantum light generation to photon-number-resolving (PNR) detection and photon number splitting (PNS) attacks on quantum key distribution (QKD). The work is structured into four main parts.
In the first part (Chapter 2), we evaluate the performance of the subtraction scheme using system parameters that are technologically accessible according to the current state of the art. We analyze the photon subtraction process in a configuration where the transitions of a Λ-type emitter are selectively coupled to the stationary modes of a bimodal cavity, which are in turn coupled to distinct waveguide modes. Using the input-output formalism of quantum optics and quantum trajectory methods, we investigate single- and multiphoton transport in the system. The results indicate that success rates approaching unity are achievable with currently reported coupling rates for cold atoms trapped in crossed optical-fiber cavities as well as for solid-state platforms based on quantum dots.
In the second part (Chapter 3), we explore the capability of the photon subtraction scheme to generate non-Gaussianity in initially Gaussian input fields. Using a photon subtractor with the emitter directly coupled to a chiral waveguide, we show that for both squeezed vacuum and coherent light input pulses, the Wigner function of the output field clearly reveals its non-Gaussian character following photon subtraction. Furthermore, we propose a measurement-based scheme on the subtracted photon which can lead to conditional generation of quantum states resembling Schrodinger’s kitten state directly from coherent input light with fidelities above 99%. This result is particularly noteworthy, as coherent pulses, unlike the squeezed vacuum inputs commonly used in previous studies, are readily available experimentally.
The last two parts of the dissertation explore the possibilities arising from cascading multiple photon subtractors. In the third part (Chapter 4), we investigate the operation of a PNR detector composed of a cascade of waveguide-coupled Λ-type emitters, which deterministically demultiplexes incoming photons among single-photon detectors. We present a closed-form expression for the detector’s precision in the linear regime and predict how correlations generated by nonlinear photon-photon interactions influence this precision. We compare the performance of the proposed PNR detector with that of a conventional PNR scheme based on spatial demultiplexing via beamsplitters. Our results indicate that the proposed scheme can outperform conventional detectors under realistic conditions, making it a promising candidate for next-generation PNR detection.
In the fourth part (Chapter 5), we present a specialized photon subtraction scheme that enables the deterministic extraction of single photons from multiphoton states while leaving input single-photon states unaltered. The proposed device consists of a two-way cascade of two Λ-type emitters coupled via a chiral waveguide. We analyze the interaction of this system with traveling few-photon pulses of coherent light and use these results to highlight how this two-emitter extension improves the original deterministic single-photon subtraction when it comes to implementing undetectable PNS attack on a QKD channel. Finally, in Chapter 6, we demonstrate how this two-emitter approach can be extended to an n-emitter cascade, resulting in a photon subtractor that selectively extracts photons from an input light stream based on their arrival time sequence. We show that this photon subtractor enables the generation of high-fidelity and modal purity multiphoton Fock states. The application of these Fock-state pulses in optical interferometry is investigated, highlighting their potential for quantum metrology at the Heisenberg limit. These results introduce novel applications of SPRINT-based photon subtraction in areas ranging from non-Gaussian photonics, to PNR detection, QKD, and quantum metrology
Study of Deep Learning Architectures for Bearing Fault Diagnosis Using STFT Spectrograms
No full textThis thesis presents a comprehensive study on vibration-based bearing fault type and severity-level detection, this process plays a critical role in predictive maintenance for industrial systems. The proposed framework combines signal processing and image-based representations derived from short-time Fourier transform (STFT) spectrograms to classify ten fault classes encompassing various fault types and severities. Among the evaluated architectures, the pretrained ImageNet model XceptionNet-71, when fine-tuned on grayscale STFTs, achieved the best overall performance, attaining a macro F1-score of 0.9979 and a mean ROC–AUC of 0.99 across all classes. This single-channel model demonstrated superior class separability compared to both flattened 1D STFT inputs and three-channel spectrograms. Which confirms that spectrogram-based representations combined with pretrained convolutional backbones are well-suited for bearing fault diagnosis and real-time deployment. While prior studies on the CWRU dataset have improved bearing fault classification through handcrafted features, lightweight CNNs, and transformer-based models, they often suffer from dataset leakage and lack systematic benchmarking. This work addresses these gaps through a unified and reproducible framework that compares 1D and 2D CNNs, extends Delta-STFT into a cross-resolution multi-channel representation, and conducts a comprehensive evaluation to classify safe versus unsafe misclassifications, bridging the gap between high accuracy and practical deployability
Monitoring risk from contaminant mixtures in stormwater with water quality measurements, bioassays, and bioassessment
No full textUrban stormwater management ponds (SWPs) are increasingly valued not only for their role in mitigating runoff but also for the biodiversity they support in densely developed environments. However, these systems receive complex contaminant mixtures from urban runoff, including pesticides, pharmaceuticals, industrial chemicals, and metals. These pollutants can accumulate in biologically active compartments like biofilms, posing risks that are not always captured by traditional water-based monitoring. My thesis investigates the nature, accumulation, and ecological effects of contaminants in SWPs using a combination of chemical, biological, and toxicological approaches.
The objectives of my research were to: (1) characterize pesticide contamination in SWPs using water, biofilm, and passive samplers; (2) quantify pesticide accumulation in biofilms and identify influencing factors; (3) assess the toxicity of contaminated biofilms through dietary exposure; (4) survey the broader suite of urban contaminants in SWPs to develop a stormwater contaminant signature; and (5) examine relationships between environmental conditions and aquatic community composition.
In Chapter 2, I surveyed 21 SWPs in Brampton, Ontario for pesticide contamination. I compared three monitoring approaches across the ponds - time-integrated water sampling, biofilm cultured on artificial substrates, and organic-diffusive gradients in thin films (o-DGT) passive samplers - finding that o-DGTs had the highest pesticide detection rates. However, issues with reproducibility in passive sampler data highlighted the challenges of using them for quantitative risk assessment. Despite generally low concentrations in water and biofilm samples, the widespread detection of diverse pesticide classes across all three matrices emphasized the chronic, mixture-based exposures in these ponds and informed recommendations for future monitoring strategies. In Chapter 3, I further investigated the use of biofilms as a sensitive and ecologically relevant matrix for contaminant monitoring. Examining a wider set of pesticide analytes, I found that over half of the pesticides detected in biofilm samples were not detected in water, suggesting that conventional sampling approaches may overlook important alternative exposure routes. Calculated bioconcentration factors (BCFs) varied widely and were not well explained by pesticide properties or water quality variables, pointing to the complexity of contaminant uptake mechanisms. To test the potential toxicity of these contaminated biofilm samples, in Chapter 4 I conducted a series of dietary exposure assays with two invertebrate grazers. Mayfly nymphs (Neocloeon triangulifer) and juvenile freshwater snails (Planorbella pilsbryi) fed with contaminated biofilms from the SWPs showed reduced survival and growth compared to controls. Although the test results did not always correlate with measured pesticide levels, these results support the ecological relevance of biofilm-mediated exposure and suggest the presence of additional stressors not captured in targeted chemical analyses. I further expanded the chemical scope in Chapter 5 by analyzing over 700 unique urban contaminants across water, biofilm, and o-DGT samples. In total, 200 organic compounds were detected, including personal care products and traffic-related pollutants, as well as persistent elevated levels of fecal indicators and chloride. From these data, I developed the Urban Stormwater Contaminant Signature (USCS): a proposed list of common, environmentally relevant compounds to guide future monitoring and toxicity testing in urban aquatic systems. Finally, in Chapter 6 I examine how environmental variables shape aquatic community composition. Diatom and macroinvertebrate assemblages sampled from the SWPs were dominated by pollution-tolerant taxa, with diatoms responding primarily to water quality (e.g., nutrients, chloride, herbicides) and macroinvertebrates more sensitive to habitat features associated with pond naturalization. Landscape-scale metrics (e.g., impervious cover) calculated from buffer zones had limited predictive power, suggesting that local conditions and upstream drainage characteristics play a stronger role in shaping biological communities.
This research highlights the need to expand contaminant monitoring in stormwater systems beyond conventional water sampling, incorporating matrices like biofilm and tools such as passive samplers to better reflect the complexities of exposures in urban environments. The detection of numerous unmonitored or rarely assessed compounds suggests that current regulatory frameworks may underestimate the complexity and risk of urban chemical mixtures. Recognizing stormwater ponds as both infrastructure and ecosystems calls for more ecologically grounded approaches to design, management, and risk assessment; ones that support biodiversity alongside water quality improvement and flood protection
Design, construction, and operation of a compact, ultrafast 100kV electron diffraction instrument
No full textElucidating the structure and properties of nanomaterials at greater resolutions necessitates the continuing development of novel imaging techniques. Electron imaging methods (such as electron microscopy/diffraction) are well-suited for probing matter at the nanoscale; for a given energy, the electron scattering cross-section is ~10⁵⁻⁶ higher than X-rays and ~10³ times less damaging [1]. Ultrafast electron imaging techniques are capable of spatial and temporal resolutions down to ~0.1 nm and 100 fs (femtosecond), respectively. This enables the observation of fundamental dynamic processes including photoinduced phase transitions, electron-phonon energy transfer, and the evolution of coherent phonons [2]. At present, open user access to the incredible power of these ultrafast techniques is generally limited to one ultrafast electron diffraction (UED) facility. Existing, well-established methods used to study nanomaterials such as X-ray diffraction and conventional electron microscopy have a plethora of commercially available, laboratory scale instruments which can be used to carry out experiments. In contrast, there are no similar turn-key devices that enable the study of ultrafast dynamic processes. The construction of an in-house ultrafast electron diffraction apparatus is one solution to the problem of instrument accessibility and the realization of time-demanding experiments with proper controls.
In this thesis, I document the design, assembly, and use of a compact laboratory scale UED instrument. The instrument is capable of stable operation at 100 kV, with subsequent development and testing suggesting that it can reach voltages in excess of ~130 kV. The instrument is able to produce electron pulses with a temporal length of ~200 fs while containing a sufficient number of electrons for adequate signal-to-noise level. Two experiments were then carried out using the UED apparatus in order to showcase its time and spatial resolutions: electron deflection by photoinduced plasma, and the investigation of the charge density wave (CDW) material NbTe2. Analysis of the time-resolved diffraction data collected from the NbTe2 measurements suggests at 60 kV demonstrate sub-picosecond resolution in agreement with the predicted instrument response obtained from N-particle tracer simulations
Costs of hydrogen production with net-zero emissions: a case study in Kitchener
No full text(© 2025 IEEE) Uwineza, L., & Wu, X. Y. (2025). Costs of hydrogen production with net-zero emissions: A case study in Kitchener. 2025 IEEE 13th International Conference on Smart Energy Grid Engineering (SEGE), 270–274. https://doi.org/10.1109/sege65970.2025.11203579This study estimates the cost of hydrogen production with net-zero emissions, focusing on life cycle greenhouse gas emissions (LCA) and including CO2 removal costs.
We design a PV-grid system for hydrogen production using HOMER’s energy modelling software with simulations based on data from a site in the city of Kitchener.
Additionally, we examine the relationship between the hydrogen production and its carbon intensity. The results illustrate that the levelized cost of hydrogen (LCOH), when including CO2 removal costs, is 9.22% to 11.39% higher compared to the LCOH without carbon removal costs. These preliminary findings can serve as potential indicators for hydrogen pricing.This project was funded in part by the Government of
Canada and the Natural Sciences and Engineering Research
Council of Canada (NSERC) [RGPIN-2021-02453]
Design of Phantoms and Targeted Agents for Ultrasound Contrast Imaging of Microvessels and Fibrin Clots
No full textVenous thromboembolisms (VTE) are common cardiovascular events including deep vein thrombosis and pulmonary embolism. Early diagnosis and management of VTEs can significantly improve patient quality of life and mortality. Such clinical insights made possible through medical imaging are critical for clinicians to manage patient risk and treatment efficacy. As the linchpin in point of care medical imaging, Ultrasound (US) is the workhorse modality in patient diagnosis and treatment monitoring within the clinic. However, specific detection of the molecule fibrin which is the current clinical treatment target for VTEs remains elusive for US in the clinic.
In this dissertation, I designed an ultrasound contrast agent with fibrin targeting properties. The baseline contrast agent was used to test a novel in vitro flow phantom with tissue mimicking characteristics to validate a contrast specific super-resolution ultrasound algorithm. The contrast agent was further modified to target fibrin leveraging the existing fibrin targeting properties of a therapeutic agent and demonstrated to be adherent to a fibrin surface as well as enhancing the detection of fibrin surfaces when imaging with a clinical scanner. Finally, a comparison study was performed examining the fibrin targeting ligand options in ultrasound literature and to further investigate the robustness of using anti-fibrin therapeutics for pure imaging applications in more physiologically realistic flow scenarios.
This work aims to raise current developments in ultrasound molecular imaging to address the existing clinical need to detect fibrin as a treatment target. Baseline MBs were shown to be versatile enablers for specialized contrast imaging applications. Moreover, the significant improvement to ultrasound signal using fibrin targeting contrast confirms that ultrasound molecular imaging can be a robust solution to improve fibrin detection. Finally, the insights gained through comparing the existing MB targeting strategies provides new directions to consider for future development of fibrin targeting contrast
Risk, reality, regulations? Finding what's reasonable in copyright guidance
No full textCopyright guidance at an academic library is often provided at the nexus of the law, university policy, and the personal and professional values of the librarians and users involved in the decision making. An institution’s tolerance for risk (or lack thereof) can create tension with librarians’ value systems. The law is often vague, leaving lots of room for differences of interpretation between University administrators, librarians, and users. Professional values, like the ones articulated by the ACRL Framework, generally align with enhancing/supporting user’s rights and tend towards a copyleft point of view. Institutional risk tolerance complicates decision making further. Higher levels of risk are generally accepted with research and teaching endeavours, directly in contrast with a lower level of risk acceptable when it comes to compliance with the law (like the Copyright Act). Lack of clarity in the law and institutional risk tolerance can be at odds with professional values, which can confuse users and undermine librarians providing guidance.
This chapter provides a beginning framework for understanding reasonableness in copyright decisions while taking into account the variety of pressures on copyright librarians. A set of cases are used to test the framework and a reasonableness chart is provided to allow for comparison of the cases