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The value of diabetes technology enabled coaching (DTEC) to support remission evaluation of medical interventions in T2D: Patient and health coach perspectives
No full text© 2025 Taylor et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.The multicomponent Remission Evaluation of Medical Interventions in T2D (REMIT) program has shown reduction of hazard of diabetes relapse by 34-43%, but could benefit from improved ability to scale, spread, and sustain it. This study explored, at the conceptualization phase, patient and health coach perspectives on the acceptability, adoption, feasibility, and appropriateness of a digital REMIT adaptation (diabetes technology enabled coaching (DTEC)). Twelve semi-structured interviews were conducted with patients (n=6) and health coaches (n=6) to explore their experiences with the REMIT study, opportunities for virtualisation, and a cognitive walkthrough of solution concepts. Transcripts were analyzed both inductively and deductively to allow for organic themes to emerge and to position these themes around the constructs of acceptability, adoption, feasibility, and appropriateness while allowing new codes to emerge for discussion. Participants saw value in DTEC as: an opportunity to facilitate and extend REMIT support; a convenient, efficient, and scalable concept (acceptability); having potential to motivate through connecting behaviours to outcomes (adoption); an opportunity for lower-effort demands for sustained use (feasibility). Participants also highlighted important considerations to ensure DTEC could provide compassionate insights and support automated data entry (appropriateness). Several considerations regarding equitable access were raised and warrant further consideration including: provision of technology, training to support technology literacy, and the opportunity for DTEC to support and improve health literacy. As such, DTEC may act as a moderator that can enhance or diminish access which affects who can benefit. Provided equity considerations are addressed, DTEC has the potential to address previous shortcomings of the conventional REMIT program.McMaster University's Population Health Research Institute, Internal Funding Program 2019-2020
Tacit Inefficiencies and Barriers in Continuous Integration
No full textContinuous Integration (CI) is the heartbeat of a software project. CI enables team members to validate each change set through an automated cycle (i.e., a CI build) that compiles and tests the project's source code. Although adoption of CI improves team productivity and software quality, these benefits come at a cost. As projects evolve, the complexity of CI pipelines tends to increase, introducing potential inefficiencies (i.e., prolonged build durations and frequent build restarts) and barriers (e.g., the specialized expertise required to maintain CI artifacts). Such inefficiencies and barriers waste resources that enable CI.
While inefficiencies and barriers in CI are often explicit, where project teams are cognizant of them, there also exist tacitly accrued inefficiencies and barriers that are not immediately apparent to project teams. In this thesis, we use historical data from a large collection of software projects to perform three empirical studies, focusing on tacit inefficiencies and barriers in CI.
We first present an empirical study that focuses on tacit inefficiencies in the environment (e.g., CircleCI) where CI builds are executed. We observe that (1) CI builds can unexpectedly time out due to issues in the environment, such as network problems and resource constraints, and (2) the history of previous CI build outcomes and anticipation of clusters of consecutive timeouts can provide useful indications to project teams to proactively allocate resources and take preventive measures. Next, we present an empirical study that investigates tacit inefficiencies in CI that stem from dependencies in projects (e.g., npm dependencies). More specifically, CI builds triggered from change sets that update versions of unused dependencies are entirely wasteful because such change sets do not impact the project source code. We find that (1) a substantial amount of CI build time is spent on these wasteful builds, (2) bots that automatically manage dependency updates in projects (e.g., Dependabot) need to consider whether a dependency is used before triggering a build, and (3) to detect and omit such wasteful builds, project teams may adopt our automated approach, Dep-sCImitar, to cut down on this waste.
We then present an empirical study that investigates tacit barriers that are related to the composition of the teams responsible for creating and maintaining CI pipelines, i.e., the DevOps contributors. In particular, we examine the diversity and inclusion of these contributors—a factor that plays a crucial role in CI by influencing collaboration and the overall efficiency of CI pipelines. Our findings show that (1) the perceived ethnic diversity of DevOps contributors is significantly low compared to other contributors, with a similar pattern observed for perceived gender diversity, and (2) the lack of diversity is amplified when considering the intersection of minority ethnicities and genders, calling for enhanced awareness of the lack of diversity among DevOps contributors
The Information Locally Stored in Quantum Fields: From Entanglement to Gravity
No full textThis thesis contains a local study of quantum field theory from fundamental, operational, and practical perspectives, with the primary goal of investigating the information that can be locally extracted from quantum fields. Central to this discussion is how the fundamental interactions of quantum fields give rise to the very objects that allow us to probe them. We approach this problem through the concept of localized quantum fields, which naturally reduce to local probes with finitely many degrees of freedom that can be accessed in realistic experiments.
Building on this detailed description of localized probes, we apply these to explore two key aspects of the information locally stored in quantum fields: entanglement and gravity. In the study of entanglement, we explore the quantification of accessible vacuum entanglement between two finite regions of spacetime. Our discussion contains both a first-principles approach based on local field degrees of freedom and an operational framework, wherein we consider the entanglement that can be harvested by coupling local probes to independent degrees of freedom of the field.
The study of entanglement in quantum field theory also leads us to classify the regimes where the quantum degrees of freedom of a field play an active role. Through the use of an effective quantum-controlled model, we show that the quantum degrees of freedom of mediating fields are only relevant in relativistic setups involving either high energies or interactions that are sufficiently localized in spacetime. In setups where these conditions are not met, a simplified effective model can accurately describe interactions while still incorporating some key relativistic elements.
Finally, we will discuss the gravitational information locally stored in quantum fields. Specifically, we will show that the correlations of quantum fields contain full information about the geometry of spacetime, and how to physically access these degrees of freedom. While the fact that quantum fields store full gravitational information might suggest the possibility of a theory in which gravity emerges directly from quantum correlations, we speculate that gravity may instead be emergent from the entanglement in quantum field theory
Digital Agent-Based Resource Management for Short Video Streaming in Multicast Networks
No full textAs fifth-generation (5G) networks approach maturity and widespread deployment, both industry and academia are turning their attention to sixth-generation (6G) networks. It is anticipated that 6G networks will support an unprecedented diversity of services with heterogeneous user requirements, accelerating the shift from service-oriented to experience-centric resource management. Among these services, short video streaming has become one of the majority of users’ daily mobile traffic consumptions due to its highly engaging content, but this also leads to substantial traffic increase, especially in densely populated areas. Considering the popularity-based and user similarity-driven recommendation principles in short video platforms, multicast transmission over the air can effectively relieve traffic pressure by delivering the same video data to a group of users with similar characteristics and locations. Quality of experience (QoE), as a subjective performance metric in experience-centric resource management, can reflect the user satisfaction level on multicast short video streaming, which usually consists of rebuffer time, video quality, and video quality variation. To achieve experience-centric resource management, digital agent (DA), as a cutting-edge technology in 6G networks, owns advanced status emulation, data analytics, and decision-making capabilities, which can perceive network dynamics, abstract hidden behavior patterns or QoE models, and solve complex optimization problems. The interesting issue is maximizing user QoE in multicast short video streaming under limited radio and computing resources within dynamic network environments. However, the main technical challenges are: (1) how DAs abstract user swipe behavior patterns for large-timescale resource reservation to enhance resource utilization and improve long-term user QoE; (2) how DAs characterize multicast buffer dynamics for real-time resource allocation to alleviate buffer length overestimation and improve real-time user QoE; (3) how to adaptively select appropriate DA models to assist resource management and timely update them to further improve user QoE.
In this thesis, we develop an efficient DA-based resource management framework to enhance user QoE for multicast short video streaming, including swipe behavior-aware resource reservation, multicast buffer-aware resource allocation, and network dynamics-aware DA management. First, we propose a DA-based resource reservation scheme by considering dynamic user swipe behaviors to enhance resource utilization and large-timescale user QoE. Particularly, user DAs are constructed for individual users, which store users’ historical data for updating multicast groups and abstracting useful information. The swipe probability distributions and recommended video lists are abstracted from user DAs to predict bandwidth and computing resource demands. Parameterized sigmoid functions are leveraged to characterize multicast groups’ user QoE. A joint non-convex bandwidth and computing resource reservation problem is formulated and transformed into a convex piecewise problem by utilizing a tangent function to approximately substitute the concave part. A low-complexity scheduling algorithm is developed to find the optimal resource reservation decisions. Simulation results based on the real-world dataset demonstrate that the proposed scheme outperforms benchmark schemes in terms of user QoE and resource utilization. Second, we propose a DA-based resource allocation scheme by considering multicast buffer dynamics to enhance real-time user QoE. In specific, user statuses emulated by DAs are utilized to estimate the transmission capabilities and watching probability distributions of sub-multicast groups for adaptive segment buffering. The sub-multicast groups’ buffers are aligned to the unique virtual buffers managed by DAs for fine-grained buffer updates. A multicast QoE model consisting of multicast rebuffer time, video quality, and quality variation is developed by considering the mutual influence of segment buffering among sub-multicast groups. A joint optimization problem of segment version selection and slot division is formulated to maximize user QoE. To efficiently solve the problem, a data-model-driven algorithm is proposed by integrating a convex optimization method and a deep reinforcement learning (DRL) algorithm. Simulation results based on the real-world dataset demonstrate that the proposed DA-based resource allocation scheme outperforms benchmark schemes in terms of user QoE improvement. Third, we develop an adaptive DA-based resource management scheme to enhance long-term user QoE. Particularly, DAs consist of user status data and data-based models, which can update multicast groups and abstract user swipe features. An adaptive DA management mechanism for DA data processing model selection and update is developed to adapt to user status dynamics. A fine-grained QoE model is established by considering the impact of resource constraints and DA model accuracy. A joint optimization problem of bandwidth and computing resource management is formulated to maximize long-term user QoE. To efficiently solve this problem, a diffusion-based DRL algorithm is proposed, which utilizes the denoising technique to improve the action exploration capabilities of DRL. Simulation results based on a real-world dataset demonstrate that the proposed adaptive DA-based resource management scheme outperforms benchmark schemes in terms of user QoE, with improvements of 18.4\% and 20.5\% under low and high user dynamics, respectively.
In summary, we have investigated DA-based radio and computing resource management from the perspectives of large-timescale resource reservation, real-time resource allocation, and adaptive DA management. The proposed approaches and theoretical results provide valuable insights and practical guidelines for experience-centric resource management in future 6G networks
Experimental Characterization and Numerical Modelling of the Energy Absorption Capacity of UD-NCF Carbon Fibre/Epoxy Composite Channels
No full textWhile the potential of carbon fibre-reinforced plastic (CFRP) composites for energy-absorbing structures has been widely acknowledged, there has been limited adoption in the automotive industry due to high manufacturing costs and challenges associated with accurately predicting their response during impact loading. Components manufactured via high-pressure resin transfer (HP-RTM) with highly reactive resins offer reduced cycle times, while unidirectional non-crimp fabric (UD-NCF) reinforcements provide a reduction in manufacturing costs, high in-plane mechanical properties, and design flexibility. However, the energy absorption capacity (EAC) of UD-NCF CFRP composites has not been widely studied, with much of the available research focused on the quasi-static axial crushing or dynamic drop-testing of unidirectional tape or woven CFRP composite tubes. Additionally, since predictive modelling is extensively used in the automotive industry, additional research on accurately modelling the EAC of CFRP composite components is required to expand their implementation in vehicle structures. Thus, the goal of this study is to support the development of a high-fidelity impact simulation model for predicting the EAC of UD-NCF carbon fibre-reinforced epoxy composite structures manufactured via HP-RTM.
Firstly, the EAC and failure modes of [0/±45/90]s, [0/90/±45]s, and [±45/02]s tapered hat channels subject to dynamic axial compressive loading were experimentally characterized. Distinct modes of failure were observed for the tested channels, with the highest energy absorption coinciding with a splaying failure mode for the [±45/02]s channels (24% to 29% higher than other layups). These results were compared to results from previously performed tests on single corrugated and hat channels comprising the same material system and tested under the same conditions. While the stacking sequence is influential for a given component geometry, manufacturing-induced defects for components with complex geometry can cause significant reductions in EAC. For all geometries, the [±45/02]s channels performed best, while the tapered hat channel exhibited up to 25% lower EAC compared to the hat and corrugated channels for this stacking sequence.
Next, simulation models were developed using the finite element software LS-DYNA to predict EAC of the tapered, hat, and corrugated CFRP composite channels with different stacking sequences. Various loading conditions were simulated, including dynamic and quasi-static axial compressive loading and three-point bending. An available material model for the progressive failure of composite laminates, MAT_054, was calibrated for a baseline case, namely the [0/±45/90]s hat channel under dynamic axial compressive loading. It was concluded that while the EAC of channels with different geometries from the baseline condition was accurately predicted when using the calibrated material model, the predictive capability of the simulation models was limited for other stacking sequences and loading rates and for the three-point bending cases. The calibrated material model was unable to capture the strain rate-dependent response of the UD-NCF composite material, which resulted in underprediction of EAC at loading rates different from the baseline condition (i.e. 33% for the [0/±45/90]s hat channel subject to quasi-static axial loading). Additionally, the single-shell part representation used in the study prevented capture of delamination, which resulted in underpredictions for energy absorption when delamination was prominent (i.e. 24% for [±45/02]s hat channels subjected to dynamic axial loading). Lastly, the EAC was significantly overpredicted for the three-point bending cases (from 13% for the [±45/02]s corrugated channels up to 60% for [±45/02]s hat channels subject to dynamic three-point bending) owing to the fact that some of the material model parameters were non-physical and thus tailored for the axial compression loading case. In general, changes in stacking sequence and loading condition required recalibration of the non-physical material model parameters; most notably, the mode-dependent failure strains.
Lastly, the energy-absorption capacity and failure behaviour of adhesively bonded double hat channels under axial compressive loading were experimentally characterized and corresponding simulation models were developed with the calibrated material model. The average total energy absorption was higher for the [±45/02]s channels when compared to the [0/±45/90]s channels with a 7.5% and 32% increase for quasi-static and dynamic cases, respectively. However, the average specific energy absorption of all double-hat channels tested was less than that measured for the single-hat channel counterpart of the same stacking sequence for both loading rates. To represent the bond numerically, the elastoplastic rate-dependent material model, MAT_240, was calibrated in LS_DYNA to experimental data and applied to cohesive elements. Predictions of EAC were more agreeable with experimental data for the dynamic axial loading cases (deviations of +11% and -21% for the [0/±45/90]s and [±45/02]s channels respectively) than the quasi-static axial loadings (deviations of -33% and -35% for the [0/±45/90]s and [±45/02]s channels respectively).
The study yielded critical performance data for UD-NCF CFRP channels and an improved understanding of the associated influence of stacking sequence, component geometry, and loading rate. The simulation results serve as a benchmark to which more physically based models (i.e., considering interlaminar interactions, strain rate dependencies, etc.) can be confidently compared
Optimizing ORAM Datastores for Scalability, Fault Tolerance, and Performance
No full textOblivious RAM (ORAM) mitigates access pattern attacks, where adversaries infer sensitive data by observing access patterns. These attacks can compromise privacy even when data is encrypted. While ORAM ensures privacy by obfuscating these patterns, its adoption in cloud environments faces significant challenges, particularly related to scalability,
fault tolerance, and performance. This thesis presents Treebeard: an ORAM-based datastore that addresses these challenges through a novel multi-layer architecture. Unlike traditional ORAM systems that rely on a centralized proxy to manage data access and security, this design separates responsibilities across specialized layers that are independently scalable. Each layer handles distinct functionalities and efficiently batches and processes requests. Treebeard facilitates horizontal scaling, and adds fault tolerance by eliminating single points of failure. Experiments show that Treebeard is scalable, highly performant, and fault-tolerant. Treebeard outperforms existing ORAM systems in terms of throughput while simultaneously addressing scalability and fault tolerance in its design
Latra: A Template-Based Language-Agnostic Transformation Framework for Program Reduction
No full textEssential for debugging compilers and interpreters, existing reduction tools face a fundamental trade-off. Language-specific reducers, such as C-Reduce and ddSMT, offer highly effective reductions but require substantial engineering effort for each target language. Conversely, language-agnostic reducers, like Vulcan, sacrifice effectiveness for broad applicability.
To bridge this gap, we present Latra, a novel template-based framework that balances both aspects, enabling general, effective, targeted program reduction. Latra combines language-agnostic reduction with user-defined, language-specific transformations. It facilitates user-defined transforms through a user-friendly domain-specific language based on simple matching and rewriting templates. This minimizes the need for deep formal grammar knowledge. Latra empowers users to tailor reductions to specific languages with reduced implementation overhead.
Evaluation shows that Latra significantly outperforms Vulcan. It reduces 33.77% more tokens in C and 9.17% more tokens in SMT-LIB, with 32.27% faster execution in SMT-LIB. Notably, Latra closely matches the effectiveness of language-specific reducers C-Reduce and ddSMT (89 vs. 85, 103 vs. 109 tokens), while significantly reducing engineering effort (167 vs. 5,508, 62 vs. 118 lines of code). We strongly believe that Latra provides a practical and cost-efficient approach to program reduction, effectively balancing language-specific effectiveness with language-agnostic generality
Statistical Analyses of Lumber Strength Properties and a Likelihood-Free Method using Empirical Likelihood
No full textWood materials should meet expected strength and reliability standards for safe and stable construction. The strength of lumber and wood products may degrade over time due to sustained applied stresses, a phenomenon known as the duration-of-load (DOL) effect. The inherent variability of lumber, combined with DOL, makes structural reliability analyses particularly challenging. This thesis develops statistical methodologies to address these challenges, focusing on reliability analysis, wood strength modeling, and likelihood-free inference.
Chapter 2 evaluates the reliability of lumber, accounting for the DOL effect under different load profiles based on a multimodel Bayesian framework. Three individual DOL models previously used for reliability assessment are considered: the US model, the Canadian model, and the Gamma process model. Procedures for stochastic generation of residential, snow, and wind loads are also described. We propose Bayesian model-averaging (BMA) as a method for combining the reliability estimates of individual models under a given load profile that coherently accounts for statistical uncertainty in the choice of model and parameter values. The method is applied to the analysis of a Hemlock experimental dataset, where the BMA results are illustrated via estimated reliability indices together with 95% interval bands.
Chapter 3 explores proof-loading experiments, another industrial procedure for ensuring lumber reliability and quality, besides the DOL experiment from Chapter 2. In proof-loading, a pre-determined load is applied to remove weak specimens, but this may also weaken the surviving specimens (survivors) — a phenomenon we term the damage effect. To capture and assess this effect, we propose a statistical framework that includes a damage model and a likelihood ratio test, offering advantages over existing methods by directly quantifying the damage effect. When applied to experimental data, the proposed framework successfully detects and measures the damage effect while showing good model fit. The framework also provides correlation estimates between strength properties, potentially reducing monitoring costs in industry.
Chapter 4 investigates statistical models with intractable likelihoods, such as the Canadian model discussed in Chapter 2. To address the challenge they pose to parameter inference, various likelihood-free methods have been developed, including a recently proposed synthetic empirical likelihood (SEL) approach. We introduce a new SEL estimator based on the reparametrization trick, which greatly reduces the computational burden. The asymptotic property of our SEL estimator is derived for the situation where the number of parameters equals the number of summary statistics, leading to a method that is not only faster, but also yields more accurate uncertainty quantification than conventional MCMC. The SEL approach is further extended by incorporating exponential tilting, which empirically improves performance when summary statistics outnumber parameters. Simulation studies validate the robustness and efficiency of our approach across various scenarios
The Effect of Acute Intermittent Hypercapnia and Exercise on Ventilatory Chemosensitivity and Cardiovascular Function
No full textVentilatory long-term facilitation (vLTF) is a form of respiratory plasticity triggered by intermittent hypoxia (IH) in the presence of CO2 (1,2,4,34). The manifestation of vLTF following intermittent hypercapnia (IHc) without concurrent hypoxia – particularly in combination with exercise- remains unclear. This study evaluated the physiological effects of IHc and moderate-intensity exercise on cardiovascular function and ventilatory control in the resting and exercising states. Twenty healthy participants (10F) completed a three-visit protocol, including two experimental exposures to either IHc (PETCO2 +5mmHg for 40s, intersped with 20s normocapnic normoxia) or continuous room air (control), each followed by an exercising and resting observational period (~45 minutes in total). At rest, the cardiovascular response to IHc was not found to be different from control. During exercise, heart rate (HR) increased following IHc and mean arterial pressure (MAP) significantly decreased (HR: +12bpm, p < 0.001; MAP: -8mmHg, p = 0.006), while both appeared stable in the time-matched control. Exercising peripheral hypercapnic chemosensitivity (PHC) appeared constant over time with IHc (+14 ± 25%), contradicting the significant decrease observed with control (-8 ± 20%, p = 0.017). While ventilation (V̇E) increased across both states following IHc relative to control, only resting V̇E was disproportionate to metabolic demand, as reflected by a lower %ΔV̇E/%ΔV̇CO2 ratio relative to control. These findings suggest the presence of exercise with IHc may have a modulatory role in the development or expression of cardiorespiratory plasticity, as well as implicating sensory long-term facilitation (LTF) as a contributor to vLTF. Finally, a progressive amplification in V̇E over the course of IHc, independent of CO2 stimulus intensity, is consistent with early-stage chemosensory gain. Collectively, these findings demonstrate that IHc without hypoxia can elicit key indicators of vLTF
Causal Inference in the Presence of Heterogeneous Treatment Effects
No full textCausal inference has been widely accepted as a statistical tool in various areas for demystifying causality from data. Treatment effect heterogeneity is a common issue in causal inference which refers to variation in the causal effect of a treatment across different subgroups or individuals within a population. This thesis explores three topics in causal inference in the presence of heterogeneous treatment effects, aiming to provide some insights for this critical issue.
Chapter 2 introduces basic notation, frameworks, models, and parameters in causal inference, serving as preliminary material for the three topics studied in Chapters 3 - 5, with a focus on the Rubin causal model.
In Chapter 3, we discuss the first topic: causal inference with survey data. In the presence of heterogeneous treatment effects, a causal conclusion based on sample data may not generalize to a broader population if selection bias exists. We propose estimators for population average treatment effects by incorporating survey weights into the propensity score weighting approach to simultaneously mitigate confounding bias and selection bias. A robust sandwich variance estimator is developed to permit valid statistical inference for the population-level causal parameters under a proposed "two-phase randomization model" framework. The proposed estimators and associated inferential procedure are shown to be robust against model misspecifications. We further extend our results to observational non-probability survey samples and demonstrate how to combine auxiliary population in- formation from multiple external reference probability samples for more reliable estimation. We illustrate our proposed methods through Monte Carlo simulation studies and the analysis of a real-world survey dataset.
Chapter 4 explores the second topic: estimation of treatment harm rate (THR), the proportion of individuals in a population who are negatively affected by a treatment. The THR is a measure of treatment risk and reveals the treatment effect heterogeneity within a subpopulation. However, the measure is generally non-identifiable even when the treatments are randomly assigned, and existing works focus primarily on the estimation of the THR under either untestable identification or ambiguous model assumptions. We develop a class of partitioning-based bounds for the THR with data from randomized controlled trials with two distinct features: Our proposed bounds effectively use available auxiliary covariates information and they can be consistently estimated without relying on any untestable or ambiguous model assumptions. Our methods are motivated from a key observation that the sharp bounds of the THR can be attained under a partition of the covariates space with at most four cells. Probabilistic classification algorithms are employed to estimate nuisance parameters to realize the partitioning. The resulting interval estimators of the THR are model-assisted in the sense that they are highly efficient when the underlying models are well fitted, while their validity relies solely on the randomization of the trials. Finite sample performances of our proposed interval estimators along with a conservatively extended confidence interval for the THR are evaluated through Monte Carlo simulation studies. An application of the proposed methods to the ACTG 175 data is presented. A Python package named partbte for the partitioning-based algorithm has been developed and is available on https://github.com/w62liang/partition-te.
Chapter 5 investigates the third topic: causal mediation analysis in randomized controlled trials with noncompliance. The average causal mediation effect (ACME) and the natural direct effect (NDE) are two parameters of primary interest in causal mediation analysis. However, the two causal parameters are not identifiable in randomized controlled trials in the presence of mediator-outcome confounding and assignment-treatment noncompliance. In such scenarios, we explore partial identification of parameters and derive nonparametric bounds on the ACME and the NDE when the treatment assignment serves as an instrumental variable. The nonparametric sharp bounds for the local causal parameters defined on the subpopulation of treatment-assignment compliers are also provided. We demonstrate the practical application of the proposed bounds through an empirical analysis of a large-scale randomized online advertising dataset.
The thesis concludes in Chapter 6 with a brief summary and discussions of future work. Technical details, including the proofs of key propositions and theorems as well as additional simulation results, are provided at the end of each chapter