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YorkSpace is York University's Institutional Repository. It supports York University's Senate Policy on Open Access by providing York community members with a place to preserve their research online in an institutional context.

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Recent Submissions

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    An IRT Model-Based Reliable Change Index With Empirical Priors: An Extension Using A Multiple Group Approach With Finite Sample Sizes
    (2025-11-11) Campbell, Sarah Grace; Robert Philip Chalmers
    The reliable change index (RCI; Jacobson & Truax, 1991) is a popular tool for assessing whether individuals have changed between treatments. Recently, an Item Response Theory (IRT)-based RCI that incorporates group mean information through the use of expected a posteriori (EAP) estimation has been adopted, showing promising results (Chalmers & Campbell, 2025). This paper extends the previous RCI-IRT work by (1) using finite sample sizes for model calibration and parameter estimation and (2) adopting a multiple group (MG) approach to modelling sample data. Results showed that even with slight methodological changes, the results are similar to the previous studies, in that incorporating empirical priors improves rates of detecting individual change when true change is present. Larger calibration sample size has an impact on model parameter recovery, but not person parameter recovery. Finally, results favour the use of the MG approach with EAP group-informed priors when underlying group heterogeneity is expected.
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    Quantum Chemistry, Machine Learning And Atomistic Monte Carlo To Study Melting-Like Transitions In Small Clusters
    (2025-11-11) Krishnadas, Anirudh; Rene Andre Fournier
    This thesis develops computational methods for efficiently predicting melting-like transitions in clusters. Four criteria are introduced to characterize these transitions: (i) the width of the potential energy distribution (W_U), which broadens near the melting point; (ii) a dissimilarity measure relative to the ground-state configuration, defined from the ordered interatomic distances r_ij; (iii) the effective count of r_ij values near the mean of the first two peaks of the pair distribution function; and (iv) the degree of non-uniformity in the r_ij distribution. Together with an artificial neural network classifier for estimating solid fractions, these measures identify melting transitions with two- to threefold reductions in computational effort compared to conventional heat-capacity-based (C(T)) analyses. Parallel Tempering Monte Carlo (PTMC) was combined with an E(3)-equivariant neural network potential (Allegro) trained to reproduce density functional theory (DFT) energies. The framework was validated on sodium clusters and applied to aluminum clusters containing up to 16 atoms in both neutral and charged states. Predicted melting temperatures ranged from 305 K to above 1200 K, exhibiting size- and charge-dependent nonmonotonic variations consistent with experimental and theoretical data. The methodology was further extended to global optimization, where tens of millions of candidate geometries were screened using the neural network, refined through local optimization, and classified by topological fingerprints and bond-orientational order. This reduced computational cost by three to four orders of magnitude compared to DFT-based sampling while maintaining near-DFT accuracy. The same approach was applied to heteroatomic icosahedral clusters AB_2C_10 chosen for their superatomic potential. Among them, ZrRb_2Au_10 and Sn_3Y_10 were prioritized based on atomization energies and electronic simplicity. Estimated melting temperatures 1263 K and 2061 K, respectively, exceeded the weighted average of their elemental melting points. Overall, this work provides new insights into cluster melting, revealing size-dependent trends, enhanced thermal stability in superatomic systems, and non-melting behaviour in certain aluminum clusters. By integrating multiple indicators and scalable algorithms, it advances the understanding of phase-like transitions at the nanoscale.
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    Measurements Of Diffusion Coefficients For Rubidium Atoms In Inert Gas Mixtures Using Coherent Scattering From Optically Pumped Population Gratings
    (2025-11-11) Pouliot, Alexander Peter Armand; Anantharaman Kumarakrishnan
    We present comprehensive determinations of the diffusion coefficients for rubidium atoms in six commonly used buffer gases using a newly developed coherent transient technique. The experiments are carried out by establishing a spatially periodic rubidium population grating using two laser beams intersecting at an angle of a few milliradians. The grating decays exponentially in time due to diffusive motion induced by momentum-changing elastic collisions with buffer gas atoms. The decay can be monitored over a large dynamic range using a heterodyne detection system that records the coherently scattered light from the grating. We are able to distinguish the contribution of diffusion from other collisional processes by measuring the characteristic dependence of the decay rate on the angle between excitation beams. These experiments are carried out in a non-magnetic atomic vapour cell manifold that allows magnetic fields and magnetic field gradients to be cancelled so that rubidium atoms can be manipulated in targeted internal ground states in the presence of different inert gases that can be maintained at pressures ranging from a few hundred pascals to one atmosphere. Our measurements agree with theoretical calculations of diffusion coefficients after reconciling key systematic effects, and this agreement appears to resolve both the widespread scatter in the values of diffusion coefficients using other techniques obtained over several decades and their disagreement with theory. Our measurements lay the groundwork for the development of a quantum pressure sensor that will rely on the intrinsic properties of atoms to calibrate commercial pressure gauges and impact emerging quantum technologies such as magnetometry, spin polarized imaging, and quantum memory that rely on accurate knowledge of diffusion coefficients. We also describe preliminary, comparative studies of a traditional population magnetometer and a unique coherence magnetometer developed by our group, which led to the development of the technique for measuring diffusion. All our experiments were carried out using a low-cost, home-built diode laser system. We present a detailed characterization of this system, which has supported wide-ranging experiments in precision metrology such as optical tweezers-based determination of micro-particle masses, measurements of atomic lifetimes, and atom interferometric measurements of velocity and gravitational acceleration.
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    Deep Learning Models for Detecting Online Harmful Content
    (2025-11-11) Wei, Feng; Uyen T Nguyen
    Deep learning (DL) has emerged as a transformative technology with substantial impact across various domains, including cybersecurity. This dissertation leverages deep learning methods and their applications to address increasingly sophisticated cyber threats. DL methods are capable of learning complex and abstract features from large-scale data, making them well-suited for identifying and mitigating cyber threats that traditional methods might miss. This dissertation focuses on the practical implementation and evaluation of DL models for detecting real-world cybersecurity threats, namely, clickbait, Twitter bots and SMS spam. Specifically, we propose: a novel attention-based neural network model named Knowledge-Enhanced Clickbait Detector (KED) that uses linguistic knowledge graphs built from WordNet to guide the attention mechanisms. The proposed neural network can effectively capture discriminative features from local and global similarities via the proposed knowledge-enhanced attention mechanisms. Moreover, we incorporate human semantic knowledge into the neural network and its attention mechanisms to better capture semantic correlations of headline-article word pairs. a novel recurrent neural network (RNN) model to distinguish Twitter bots from human accounts based on textual content of their tweets. We use several types of linguistic embeddings to encode tweets, namely, word embeddings, character embeddings, part-of-speech embeddings, and named-entity embeddings. We avoid using handcrafted features, which require time-consuming and labor-intensive feature engineering. This advantage allows for faster and easier implementation and deployment of the bot detection scheme. a novel lightweight deep neural model called Lightweight Gated Recurrent Unit (LGRU) for SMS spam detection. We incorporate enhancing semantics retrieved from external knowledge to assist in understanding SMS text inputs for more accurate detection. In addition, the lightweight model illustrates a method to minimize unnecessary complexity in training recurrent models without compromising the performance, which we believe is applicable to many other complex recurrent models for other applications. Experimental results show that the above models outperform their counterparts, including state-of-the-art models/systems and other baseline models, in terms of predictive performance and/or running time. The proposed models provide robust, scalable, and real-time security solutions that can adapt to the rapidly changing landscape of cyber threats.
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    Barriers And Facilitators To Hemodialysis Access And Utilization Among Patients With End-Stage Kidney Disease In Ghana: A Mixed-Methods Study From The Perspectives Of Patients, Providers, And Administrators.
    (2025-11-11) Japiong, Milipaak; Christine Kurtz Landy
    ABSTRACT Background: Chronic kidney disease (CKD) is a major and growing public health issue in Ghana, with a prevalence of 13.3%. Many individuals with CKD progress to end-stage kidney disease (ESKD), for which hemodialysis is the principal lifesaving treatment. However, access to and utilization of hemodialysis remain severely limited by financial, geographic, and systemic barriers, including high out-of-pocket costs, inadequate insurance coverage, and the uneven distribution of dialysis centers. Despite the increasing burden of ESKD, limited evidence explores the multifaceted barriers to hemodialysis in Ghana from the perspectives of patients, healthcare providers, and hospital administrators. Purpose: This dissertation aims to explore and identify the barriers and facilitators to delivering quality care for individuals with ESKD, with a focus on access to and utilization of hemodialysis in Ghana. Methods: A sequential explanatory mixed-methods design was employed. The quantitative phase involved a cross-sectional study of 264 patients with ESKD, using structured questionnaires. Predictors of hemodialysis access and utilization were identified through bivariate and multivariate logistic regression analyses using SPSS version 27. The qualitative phase employed maximum variation purposive sampling to recruit 16 adults with ESKD (receiving or in need of hemodialysis) and 16 healthcare providers/administrators from two teaching hospitals. Data from semi-structured interviews were analyzed thematically using Braun and Clarke’s framework with NVivo 14. Results: Among the 264 patients, 74.2% had hemodialysis access, but only 38.8% regularly adhered to all prescribed sessions. Greater access and utilization were associated with higher income, urban residence, longer duration since diagnosis, and distance to dialysis centers. Qualitative findings revealed shared perspectives across stakeholder groups: individual factors (resilience, family support) facilitated care, while systemic challenges high treatment costs, facility shortages, workforce constraints, limited insurance coverage, and geographic disparities, impeded consistent hemodialysis access. Participants emphasized the need for coordinated multi-level interventions to address these interlinked barriers. Conclusion: Access to and utilization of hemodialysis in Ghana are shaped by intersecting financial, geographic, systemic, and sociocultural challenges. Addressing these issues requires comprehensive reforms, including increased public investment, infrastructure expansion, targeted health education, and equity-focused health policies to ensure sustainable and equitable dialysis care for patients with ESKD. Keywords: Hemodialysis; access; utilization; barriers; Ghana; chronic kidney disease; mixed methods; equity; health services.