eScholar

eScholar stores, preserves and disseminates digital copies of the research and scholarly output of eScholar faculty, researchers and students. These can include the following items:

  • Monographs
  • Pre- and post-prints of academic journal articles
  • Theses and dissertations
  • Major projects and papers
  • Reports/working papers and conference proceedings

Materials in eScholar are openly available to the world and discoverable through search engines such as Google Scholar. This high visibility, discoverability, and exposure can lead to increased citation. Contact Library Publishing for more information.

Communities in eScholar

Select a community to browse its collections.

Now showing 1 - 5 of 9

Recent Submissions

  • Thumbnail Image
    Item type: Item , Access status: Open Access ,
    Toward automated requirements engineering: empirical and architectural foundations for structured parsing and knowledge discovery
    (2026-05-01) Patel, Dvip; Alwidian, Sanaa
    Requirements Engineering (RE) relies heavily on natural language, which is often vague, inconsistently structured, and difficult to automate reliably. This thesis presents TRAC-RE, a Traceable, Reliable, Auditable, and Contextual framework for automated requirements engineering. The framework consists of four interconnected phases addressing requirement identification, audit-grade keyword extraction, structured large language model (LLM) parsing, and implicit keyword discovery. A context-aware requirement identification model improved F1 from 0.664 to 0.894 on 110 real-world FinTech and SaaS documents. An audit-grade keyword extraction pipeline achieved perfect precision while revealing a structural coverage ceiling of 1.5 canonical keywords per artifact. A governed High-Level JSON (HLJ) parsing pipeline improved tag precision from 0.657 to 0.897. Finally, a multi-signal implicit discovery engine grounded in a 13,725-entry domain dictionary addressed limitations of explicit extraction. Together, these contributions establish empirical and architectural foundations for reliable, structured, and auditable automated requirements engineering.
  • Thumbnail Image
    Item type: Item , Access status: Open Access ,
    The impact of subclinical neck pain on cerebellar processing and task familiarization as measured by the vestibulo-ocular reflex and cervical proprioception
    (2024-08-01) Misketis, Christine; Murphy, Bernadette
    Cerebellar processing is altered in individuals with subclinical neck pain (SCNP). The vestibulo-ocular reflex (VOR), a direct measure of cerebellar function, can evaluate these changes. Task familiarization impacts task performance accuracy, but this hasn’t been assessed in VOR gain adaptation. This thesis investigates differences in VOR gain adaptation, corrective eye movements, and cervical proprioception in SCNP vs healthy controls using eye-tracking and cervical proprioception protocols. It then examines task familiarity effects on these measures after 8-weeks of normal activities. Individuals with SCNP showed increased corrective saccades during VOR gain adaptation without changes in VOR gain itself compared to healthy individuals. Both groups showed a decrease in corrective saccades at 8-week follow-up, with no relative difference between groups. This thesis suggests that SCNP individuals exhibit greater error in VOR gain adaptation which does not spontaneously recover over time.
  • Thumbnail Image
    Item type: Item , Access status: Open Access ,
    Experimental and analytical investigation of a new integrated reactor for hydrogen and methanol production with ocean carbon dioxide
    (2025-06-01) Akci Turgut, Hilal Sayhan; Dincer, Ibrahim
    This study introduces and evaluates a novel integrated three-compartment electrolytic cation exchange membrane (E-CEM) reactor designed for the simultaneous extraction of carbon dioxide (CO₂) from ocean water and the generation of hydrogen (H₂), with the goal of green methanol synthesis. The E-CEM reactor operates through electrochemical acidification and cation exchange processes to convert oceanic bicarbonates and carbonates into gaseous CO₂, while concurrently producing hydrogen via ocean water electrolysis. A maximum CO₂ extraction rate of 1514.60 mg/min is achieved under optimized conditions, 13–14.80 V, 1.80–2.0 M electrolyte concentration, and pH 2.2–3.0. Hydrogen generation increases to 2.07 mg/min at 12.80 V with 1.70 mol/L concentration. Further optimization of raising the concentration to 1.85 mol/L and reducing the pH to 2.0 increases H₂ production to 2.20 mg/min. In terms of sustainability, the E-CEM reactor demonstrates a 20% higher exergetic sustainability index compared to the peristaltic pump under ambient pressures ranging from 100 to 1000 kPa. In methane production tests using an H-cell setup, 1.5 M electrolyte yields the highest output, reaching 900 ppm during early stages. H-cell methanol synthesis, confirms via the acetylacetone spectrophotometric method, shows improved yields with increasing electrolyte concentration: 5.50 mg/L at 0.5 M, 7.20 mg/L at 1.5 M and 8.70 mg/L at 2.0 M. In the integrated H-cell and E-CEM configuration, methanol production reaches 4.20 mg/L at 7 V. Overall, the integrated E-CEM system demonstrates an energy efficiency of 7% and an exergy efficiency of 9%. While these values demonstrate promising performance, they also indicate room for further optimization. The E-CEM reactor is therefore placed as a promising and scalable green solution for sustainable CO₂ utilization and hydrogen-based energy systems.
  • Thumbnail Image
    Item type: Item , Access status: Open Access ,
    Multi-dimensional readiness signals for structured and LLM-assisted requirements inspection
    (2026-06-01) Alsafadi, Sali; Alwidian, Sanaa; Elgazzar, Khalid
    Effective software quality assurance requires that development teams inspect large collections of requirements before implementation begins, yet no existing tool helps them decide where to focus their limited inspection effort. The inspection risk of any given requirement is not a property of that requirement in isolation. Rather, it is determined by other factors, including how each requirement relates to others in the requirements specification document, how structurally central it is, how clearly it is written, and how likely it is to change. Such factors are invisible when requirements are examined individually. Moreover, the prevailing practice of collapsing these dimensions into a single weighted score through compensatory aggregation destroys the diagnostic value of each dimension independently. This thesis proposes and empirically evaluates a two-layer inspection support system. The first layer, Prioritised Requirements Inspection through Signal Metrics (PRISM), computes three collection-level readiness metrics (structural centrality, linguistic specificity, and volatility exposure), and combines them through Pareto dominance-based structuring. Rather than collapsing these orthogonal metrics into a single scalar score, PRISM preserves each dimension independently, yielding a multi-dimensional inspection ordering. Evaluated on 262 requirements, the metrics are statistically independent, and the ordering aligns significantly with independent human inspection judgment (Spearman ρ = −0.478, ρ = 0.0037), outperforming all single-metric baselines. The second layer, PRISM-Copilot, instantiates the Signal-Conditioned Inspection Prompting (SCIP) architectural pattern, which leverages the validated PRISM metrics to condition an LLM-based inspection assistant by directing each requirement toward the type of scrutiny most relevant to its specific risk profile. Rather than subjecting all requirements to undifferentiated general-purpose inspection prompts, SCIP maps each requirement’s signal profile to a targeted inspection policy, focusing the LLM’s reasoning on the concern dimensions where collection-level evidence indicates the greatest risk. A proof-of-concept study on 50 requirements demonstrates that this targeted signal conditioning causes the LLM to raise change-sensitivity concerns where volatility metrics support them and suppress them where they do not, providing empirical evidence that collection-level metrics can meaningfully steer LLM inspection reasoning. The two layers constitute a principled, end-to-end inspection support architecture that is context-aware at the collection level rather than the requirement level, establishing a practical foundation for both systematic inspection prioritization and signal-conditioned AI-assisted inspection reasoning in large-scale software engineering practice.
  • Thumbnail Image
    Item type: Item , Access status: Open Access ,
    Colonial legacies and the trafficking of Indigenous women in Canada
    (2026-04-01) Mazzotta, Olivia; Cesaroni, Carla; Zaidi, Arshia
    This paper examines how the ongoing impact of colonialism continues to shape the vulnerability of Indigenous women and girls to human trafficking in Canada. It offers a decolonial critique of Canada’s National Strategy to Combat Human Trafficking, while drawing on Critical Race Theory and Indigenous feminist thought. This analysis finds that current legislation inadequately addresses root causes of trafficking and lacks inclusion of Indigenous voices. This paper argues that meaningful change requires Indigenous-led frameworks, redistribution of power, and a commitment to decolonizing existing systems. By highlighting these gaps, this research contributes to human trafficking literature by emphasizing the necessity of Indigenous leadership in developing effective policy solutions.