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Development of Clinical Pharmacy Key Performance Indicators for Ambulatory Oncology Using a Modified Delphi Approach
No full textIntroduction: Ambulatory Oncology Clinical Pharmacy Key Performance Indicators (AOcpKPIs) are quantitative measures that reflect the quality of care provided by pharmacists to ambulatory oncology patients. In this research project, AOcpKPIs were selected to advance pharmacy practice to improve patient outcomes. Research completed prior to this work involved a cross-sectional survey as part of a Canadian environmental scan. This survey revealed that 98% of respondents supported the development of AOcpKPIs for ambulatory oncology pharmacy practice. The primary objective of this project was to identify targeted processes of care aimed at improving ambulatory oncology patient outcomes represented by critical activity topics (pharmacist interventions), which were used to develop quantifiable AOcpKPIs.
Methods: A literature review on evidence-informed critical activity topics revealed that certain care processes delivered by ambulatory oncology pharmacists had a positive impact on patient outcomes. A steering group comprised of expert ambulatory oncology pharmacists created evidence summary tables by critically appraising and assessing articles to identify “Ambulatory Oncology Critical Activity Topics”. A list of 15 candidate AOcpKPIs was generated by the steering group to serve as metrics to represent 9 evidence-linked critical activity topics. A sample of 18 pharmacists was purposively selected by the steering group to participate in a Delphi panel with the goal of reaching consensus on AOcpKPIs to adopt.
This research project employed a modified Delphi process (a structured method to reach consensus among experts through multiple rounds of surveys and discussion in a meeting) where panelists ranked the a priori selected candidate AOcpKPIs on a 9-point Likert scale in the initial round and then reviewed the rankings and comments of the first round to re-rank the 15 AOcpPKIs in round 2. A final consensus was reached in the third round following a live meeting discussing marginal candidate AOcpPKIs from the second round.
Results: After the first round of the Delphi process, 9 candidate AOcpKPIs reached preliminary consensus. This number increased to 12 after the second round. During a live meeting, discussions about the wording and meaning of the candidate AOcpKPIs led to the addition of 2 new candidates, totaling to 17 for the third round. Following the third round, 14 AOcpKPIs achieved consensus. These cpKPIs highlight essential pharmacist contributions, such as reviewing clinical orders, providing patient education on medications, assessing medication adherence, pharmaceutical care plan (oncology), pharmaceutical care plan (non-oncology), follow-up, bundled patient care interventions, medication history assessment and addressing drug therapy issues.
Significance: This research project identified 14 quantifiable AOcpKPIs, based on ambulatory oncology patient outcomes, that represent critical activity topics. Through a systematic, evidence-informed consensus-building process, the final suit of consensus AOcpKPIs that improve patient outcomes were formed. Future efforts will focus on implementing these metrics in ambulatory oncology pharmacy practice to measure and ensure the quality of patient care
Data-Based Modeling of Electrochemical Energy and Thermal Systems: Fuel Cell and Lithium-Ion Battery
No full textAs a solution to combat climate change and environmental pollution, electrochemical energy systems such as Proton Exchange Membrane Fuel Cell (PEMFC) and Lithium-Ion Battery (LIB) are being developed as the replacement for fossil fuel-powered combustion engines, especially for ground transportation and aviation applications. These electrochemical energy systems must be able to operate independently and in conjunction with each other by complementing their advantages and limitations, such as efficiency, range, thermal behavior, aging, and operating environment. This interoperability requires accurate real-time computational models to control, diagnose, and adapt according to field requirements. A typical electrochemical energy system model needs to incorporate effects related to reactant concentrations, system overpotentials, thermodynamics, porous media mechanics, membrane dynamics, gas diffusion, electrode degradation, electrolyte status, ion transport, and chemical kinetics across various operating conditions, all of which result in complex interactions affecting the accuracy and reliability of the system.
Today, both PEMFC and LIB use complex computational physics-based fluid dynamics models in the product development phase, which requires enormous computational power and long lead times for iterative prototype improvements. On the other hand, both PEMFC and LIB rely on simple lookup tables and semi-empirical equations as plant models that require intensive calibration activity to determine the mode of control and diagnosis for automotive applications. However, considering the present-day automotive propulsion systems, which operate in widely varied applications and geographic locations and have short product development cycles, these approaches are not able to comprehend the complexities, hindering the ability of these systems to operate at their full potential and leading to catastrophic failures (e.g., Thermal runaway). Data-based modeling techniques are one of the potential solutions, which is quite in contrast with other empirical or physics-based models where the entire input-output relations of the model are established primarily based on the data. Data-based models use aspects of statistics, probability, and network architecture, avoiding the complexities of physics-based models and intensive calibration, providing better accuracy in most cases, primarily where the complex mechanisms can’t be modeled using specific governing equations, and fast, efficient computation with much less computational resource requirement.
This thesis focuses on data acquisition (identifying and collecting the relevant data) and data-based model development by incorporating machine learning algorithms and regressors to predict the system's performance, thermal behavior, aging, and faults in real-time (on-board diagnostics). Data for these models is acquired through two approaches: experimentation by utilizing Fuel Cell and Green Energy Lab facilities such as the Automated Battery Test Station (ABTS), G20 fuel cell automated test station, and by partnering with the relevant industry. In the second approach, data is generated by simulation of physics-based models (CFD, Semi-empirical, equivalent circuit models) that are experimentally validated in the literature and developed within the research groups of UWaterloo. Development of a data-based model includes the identification of feature vectors (inputs), prediction attributes (outputs), state estimates (internal parameters), non-linearity of the systems, correlation factors of various system entities, and application of machine learning techniques such as feed-forward artificial neural network, support vector machine classifier - regressor, along with their respective adaptations and calibration processes. The primary objectives of this study are to develop data-based models for three main application areas: (i) Prediction of PEMFC performance, internal states of the membrane, cell voltage degradation, and system outputs. (ii) Prediction of LIB heat release rate during discharge and thermal dynamics of an open system during an exothermic reaction. (iii) Prediction of fuel cell battery hybrid electric vehicle’s system dynamics and thermal behavior.
During this study, various data-based models were developed to tackle the problems encountered in fuel cell-battery hybrid systems, such as predicting the fuel cell performance, fuel cell voltage degradation, PEMFC membrane dynamics, lithium-ion battery thermal dynamics, thermal behavior during exothermic reactions and dynamics of fuel-cell battery hybrid system. The results presented in this study proved the data-based model’s applicability in surrogate modeling, real-time system monitoring, controls, and diagnostics of electrochemical energy systems both at the component level and system level. Additionally, the results implicate that the data-based model can serve as a complement and alternative to the traditional computational fluid dynamics models as well as complex physics-based and empirical models to predict thermal gradients and system internal states during multifaceted reactions
Large Language Models for Build System Maintenance: An Empirical Study of CodeGen’s Next-Line Prediction
No full textBuild systems play a crucial role in software development and are responsible for compiling source code into executable programs. Despite their importance, build systems often receive limited attention because their impact is not directly visible to end users. This oversight can lead to inadequate maintenance, frequent build failures, and disruptions that require additional resources. Recognising and addressing the maintenance needs of build systems is essential to preventing costly disruptions and ensuring efficient software production.
In this thesis, I explore whether applying a Large Language Model (LLM) can reduce the burden of maintaining build systems. I aim to determine whether the prior content in build specifications provides sufficient context for an LLM to generate subsequent lines accurately. I conduct an empirical study on CodeGen, a state-of-the-art Large Language Model (LLM), using a dataset of 13,343 Maven build files. The dataset consists of the Expert dataset from the Apache Software Foundation (ASF) for fine-tuning (9,426 build files) and the Generalised dataset from GitHub for testing (3,917 build files). I observe that (i) fine-tuning on a small portion of data (i.e., 11% of fine-tuning datasets) provides the largest improvement in performance by 13.93% (ii) When applied to the Generalised dataset, the fine-tuned model retains 83.86% of its performance, indicating that it is not overfitted. Upon further investigation, I classify build-code content into functional and metadata subgroups based on enclosing tags. The fine-tuned model performs substantially better in suggesting functional than metadata build-code. The findings highlight the potential of leveraging LLMs like CodeGen to relieve the maintenance challenges associated with build systems, particularly in functional content. My thesis highlights the limitations of large language models in suggesting the metadata components of build code. Future research should focus on developing approaches to enhance the accuracy and effectiveness of metadata generation
Assessment of the Proposed Policies for a Carbon Capture and Storage Regulatory Framework in Ontario
No full textSince 2022, Ontario has been investigating the possibility of developing a Carbon Capture and Storage (CCS) framework as they aim to reduce carbon emissions and align with the federal government’s goals of net-zero emissions by 2050. This CCS regulatory framework should focus on hard-to-abate sectors where alternative renewable energy technologies are in their early stages, or they are difficult to be transitioned. However, within the research field of CCS in Ontario from a policy perspective, there are minimal journal articles and grey-literature documents that discuss this topic. Therefore, the purpose of this thesis is to understand and analyze Ontario’s proposal of their regulatory framework for CCS and to give recommendations to the CCS framework by comparing it against the information gathered from other jurisdictions (Alberta, Saskatchewan, the United States, Europe and Australia). Key research questions are 1. How can the knowledge gained from other regions regarding CCS help Ontario's hard-to-abate sectors to understand approvals, licensing, and liability? 2. What are some other necessary policies that Ontario would need to expand upon and potentially adopt from various jurisdictions? And 3. How did companies and governments in other jurisdictions communicate to the public about the need for this technology? The thesis first developed a literature review to compare and contrast policies from other jurisdictions by researching and synthesizing various peer-reviewed journal articles and grey literature. Then, a semi-structured interview was needed to explore any unique perspectives from interviewees with expertise in CCS, and also to understand whether the results aligned with the information from the literature review. Following the interviews, the analysis of the results were accomplished by using ‘codes’ and ‘themes’, which allows for a simplified understanding of which information is unique. As a result, there were unique findings from the interviews such as ensuring proper industries are utilizing CCS, explaining the purpose of CCS, ensuring that the regulatory framework for CCS is properly developed, and the potential for CCS to utilize a carbon market through an Emissions Trading System (ETS). In November 2024, Ontario introduced Bill 228, which contains an Act called the Geologic Carbon Storage Act, 2024. This Act contains the key core components of the regulatory framework, such as ownership, liabilities, and approvals and assessments. As a result, a description and analysis of this Act was undertaken to understand how it compares against my research findings. In conclusion, to answer the first research question, the findings resulted in requiring Ontario to vest in the pore space, implement a unitization statue, implement a transfer of liabilities once certain pre-conditions are met and a post-stewardship fund to cover liability costs. As for the second research question, the other necessary policies include expanding upon environmental assessments methods, using a systems analysis approach to understand the outcomes of developing CCS, incorporating CCS into carbon pricing schemes, and Ontario’s plans on how they should utilize their CCS. The findings for the final research question recommend that the Ontario government and companies recognize the social demographic backgrounds of Ontario; ensure that Ontario is integrating and engaging with communities closely; explaining the downsides of not developing a CCS project; and respecting a community’s decision if they do not wish to engage with the project. Bill 228 is consistent with these findings, namely the inclusion of a liability transfer; a stewardship fund to cover the liabilities for the Crown; unitization of pore spaces; risk management; monitoring, measurement and verification (MMV); emergency response; and various approvals and assessments. However, the ownership of pore spaces deviates from these findings, as Ontario vests pore ownership to the surface owners but still allows the Crown to vest in the pore space when required
Development of carbon-based materials for improved sodium-ion battery anodes
No full textSodium-ion batteries (SIBs) are an important future technology for large-scale grid energy storage due to the abundance and lower cost of sodium compared to lithium. However, there are many challenges that continue to hamper their commercial development, including sluggish reaction kinetics, large volume expansions during cycling, and unstable solid-electrolyte interface (SEI) formation. Additionally, more work needs to be done to understand the storage mechanisms of sodium ions in anode materials and to understand what material properties are important for high performance. This thesis focuses on two areas: the development of a new anode material made of red phosphorus nanoparticles (RPNPs) wrapped in reduced graphene oxide (rGO) sheets and an in-depth investigation into the impact of rGO material characteristics on its performance as a SIB anode.
In the first section, the synthesis of an rGO@RPNP composite through a scalable spray drying process provides increased performance when compared to the individual components or when the individual components are simply mixed together. This proof of concept is the starting point for future work on increasing red phosphorus loading within the core-shell structures, improving the reduction process to maximize the conductivity of the composite, and investigating methods to improve the initial coulombic efficiency (ICE) through optimization of material properties.
The second section attempts to decouple the effects of rGO chemistry and oxygen content from its surface area to understand the effect of the material characteristics on its electrochemical performance. It is found that a non-exfoliated sample slowly reduced to a low temperature of 400 ℃ provides the best performance in terms of desodiation capacity (216 mAh g-1 at 100 mA g-1), and stability (85% retention over 200 cycles) while reducing the irreversible capacity loss by two to threefold compared to previous literature
Investigating the Dynamics of Meandering River Cutoffs: Relationships with Discharge, Land Cover and Spatial Clustering
No full textAs climate change has become one of the major concerns across the globe, investigating the dynamics of meandering river evolution is substantial for urban river management and flood mitigation plans. In recent years, the study on river cutoff has been given lots of attention, as its occurrences and impacts were unpredictable and catastrophic. This study investigates its relationship with high-flow events, land cover and spatial clustering through flood frequency analysis, cutoff ratio criterion and spatial cluster analysis. 1,186 river cutoffs across the United States are located and identified based on Google Earth Imagery. 12 highly sinuous rivers with high cutoff occurrences are then selected and processed through R Studio and ArcGIS. The results show no strong correlation between high-flow events and cutoff occurrence across the study areas. Discharges with an average of approximately eleven-year return period are associated with cutoff occurrences. With the installation of the cutoff ratio in the dataset, it is found that chute cutoffs with higher CRm_m values are likely to occur on land cover types with lower erosion resistance. Neck cutoffs are usually found in floodplains less susceptible to erosion, particularly in undisturbed vegetated areas. Spatial cluster analysis shows that neck cutoffs are significantly clustered at all scales, whereas chute cutoffs exhibit relatively lower clustering tendencies and tend to be more event-driven. Minimizing the random disturbances in the analysis, this study collectively validates the non-random behaviour of cutoff occurrences, which further calls attention to the importance and viability of assessing and predicting cutoff evolution in urban planning and flood management
Advancing sustainable packaging: The role of nanofibers in bioplastics
No full textIn the last few decades, the packaging industry has become one of the fastest-growing industries worldwide, owing to some changes in standards of living, consumption habits, and global trade expansion. Bio-based materials have emerged as one of the most interesting subjects of research in the packaging industry due to the environmental concerns associated with materials derived from petrochemical sources, such as resource depletion, recycling challenges, and biodegradation which have resulted in the development of eco-friendly materials.
Polymers are widely used in the packaging industries, and synthetic polymers are extensively employed mainly because of their outstanding mechanical properties, effective barriers against oxygen and water, and ease of processability. However, they present significant downsides, such as poor degradability and challenges in recyclability, and as a result, packaging waste constitutes a large portion of post-consumer solid waste, leading to ecological problems. Therefore, extensive research is being conducted to develop biopolymers in the packaging industry. The challenges associated with the global usage of biopolymers include poor mechanical and barrier properties and high production costs. In order to modify the properties of biopolymers, various methods could be employed, such as reinforcing the polymer matrix with nanomaterials, especially nanofibers.
The first goal of this project was to optimize the process of preparing nanofibers derived from hemp. Pre-treatments were applied before the fibers underwent the refining process to reduce the number of steps required for refinement and to investigate their effect on the stability and diameter of the nanofibers produced. This approach not only saves time, energy, and costs but also enhances the overall efficiency of the process, representing a significant step forward for the industry.
In this study, mechanical treatment was applied for the fibrillation of hemp fibers. This method has significant advantages over chemical treatment, particularly in terms of reducing the amount of chemicals used. This aligns with one of the most important goals of this project, promoting a more sustainable and cost-effective approach.
In order to improve the efficiency of the fibrillation process, several pre-treatments were applied. Among them, the pre-treatment involving fiber hydration by immersing the fiber in water for one hour, subjecting it to a strong vacuum for 30 minutes, and processing it in a pressure cooker at high temperature (≈120°C) and pressure (12 psi) for 10 minutes resulted in the smallest fiber size reduction after eight passes. Furthermore, based on the stability test, this sample exhibited the highest stability, remaining stable after seven days.
Another goal of this project was to improve the mechanical and barrier properties of biodegradable nanocomposite films for packaging applications. Polybutylene succinate (PBS) has high flexibility, high elongation at break, good biodegradability, and water resistance. However, due to its low molecular weight, low stiffness, poor oxygen resistance, and high cost, its potential applications are limited. Therefore, one solution could be the addition of hemp nanofibers (HNF) to PBS in order to enhance biodegradation and reduce costs. In this study, nanocomposites of PBS and HNF, with a ratio of 95/5, were first prepared using an extruder and hydraulic press. Their barrier and mechanical properties were then investigated. Then, these properties were compared with the properties of nanocomposites containing PBS/HNF with the addition of beeswax and sodium dodecyl sulfate (SDS) at different ratios.
The moisture content, water absorption capacity, and water solubility tests showed that adding beeswax reduced moisture content, water absorption, and water solubility. These effects became more pronounced with increasing amounts of beeswax. Similarly, introducing SDS as a surfactant resulted in a greater decrease in these properties compared to adding beeswax alone, with further reductions observed as the concentration of SDS increased.
Furthermore, the results of the water vapor permeability (WVP) test revealed that the incorporation of nanofibers resulted in a decrease in the film permeability due to its hydrophilic nature. However, beeswax created a barrier that hindered the movement of water vapor molecules through the film due to its hydrophobic nature. The extent of this decrease depends on the amount and distribution of the beeswax. When SDS was introduced to the film’s formulation, its bridging effect could further reduce the WVP amounts of films, though only at low SDS concentrations. Overall, the interactions between all components (PBS, hemp nanofiber, beeswax, SDS) can influence the final film structure.
Additionally, mechanical tests demonstrated that adding HNF to PBS films increased tensile strength and modulus. However, this led to a decrease in elongation at break. For samples with beeswax in the formulation, the flexibility of the films increased, resulting in an increase in the film's elongation. In terms of tensile strength and tensile modulus, beeswax improved the compatibility between PBS and HNF. This enhancement led to better dispersion of hemp nanofiber within the PBS matrix, resulting in a more uniform composite and improved tensile strength. Meanwhile, the addition of beeswax to the formulation, due to its plasticizing effect, is expected to reduce the tensile modulus. In the final compositions, SDS was added to the film formulation. At low concentrations, SDS behaves as a surfactant, reducing the surface tension between PBS and HNF. This leads to a better dispersion of the nanofibers throughout the PBS matrix, which could reduce stress concentration. Well-dispersed nanofibers create a more uniform stress distribution within the film. This can help prevent premature failure at specific points and allow for more stretching before breaking, potentially increasing elongation. While SDS aids in dispersion at lower concentrations, excess SDS can interact with the surfaces of both PBS and HNF, disrupting the natural interactions (such as hydrogen bonding) between them, which contribute to the overall strength and integrity of the film and their disruption can make the film more susceptible to breaking under stress, potentially leading to decreased elongation. Meanwhile, the initial addition of SDS enhanced the composite's tensile strength mainly because of improved PBS-HNF adhesion and better stress transfer from the polymer matrix to the fibers. The addition of SDS over the optimal concentration resulted in phase separation, which could be regarded as a weak point in the composite, thereby having an adverse effect on the tensile strength. It was observed that by introducing SDS to the formulation tensile modulus also decreased.
Overall, the nanocomposites prepared exhibited promising properties for sustainable packaging applications. Nevertheless, additional research and development are essential to improve and optimize the material properties further for optimal performance
Data-Driven Predictive Control: Equivalence to Model Predictive Control Beyond Deterministic Linear Time-Invariant Systems
No full textIn recent years, data-driven predictive control (DDPC) has emerged as an active research area, with well-known methods such as Data-enabled Predictive Control (DeePC) and Subspace Predictive Control (SPC) being validated through reliable experimental results. On the theoretical side, it has been established that both DeePC and SPC methods can generate equivalent control actions as one can obtain from Model Predictive Control (MPC), for deterministic linear time-invariant (LTI) systems.
However, similar results do not yet exist for the application of DDPC beyond deterministic LTI systems. Therefore, the objective of our research is to generalize this theoretical equivalence between model-based and data-driven methods for more general classes of control systems.
In this thesis, we present our contributions to DDPC for linear time-varying (LTV) systems and stochastic LTI systems. In our first piece of work, we developed Periodic DeePC (P-DeePC) and Periodic SPC (P-SPC) methods, which generalize DeePC and SPC from LTI systems to linear time-periodic (LTP) systems, as a special case of LTV systems. Theoretically, we demonstrate that our P-DeePC and P-SPC methods have equivalence control actions as produced from MPC for deterministic LTP systems, under appropriate tuning conditions. As an intermediate step in our theoretical development, we extended certain aspects of behavioral systems theory from LTI systems to LTP/LTV systems. This includes extending Willems’ fundamental lemma to LTP systems and the defining the concepts of order and lag for LTV systems.
In our second piece of work, we proposed a control framework for stochastic LTI systems, namely Stochastic Data-Driven Predictive Control (SDDPC). Our SDDPC method theoretically achieves equivalent control performance to model-based Stochastic MPC, under idealized conditions of appropriate tuning and noise-free offline data. This method, which applies to general linear stochastic state-space systems, serves as an alternative to the data-driven method previously proposed by Pan et al., which also achieved theoretical equivalence to Stochastic MPC but was limited to a narrower class of systems. Beyond the theoretical assumption of noise-free offline data, we performed our SDDPC method in simulations with practical noisy offline data. The simulation results demonstrated that our SDDPC method outperforms benchmark methods, achieving lower cumulative tracking cost and lower rate and amount of constraint violation
Engineering a Lasting Peace for Gaza and the Middle East
No full textAn economic corridor through Gaza — the India-Middle East-Europe Economic Corridor (IMEC) — could become a gradual pathway for peace, productivity and stability in the region
"I Could be Suborned with a Sardine": A Material Culture Study of Teresa of Ávila's Letters
No full textEarly modern Carmelite reformer and mystic Teresa of Ávila (1515-1582) maintained an extensive correspondence, which has received less scholarly attention than her major autobiographical and mystical works. Her letters reached a broad audience, from her brother and later her nephew in colonial Peru, to her advisor Jerónimo Gracián, to the prioresses of the convents she founded across Spain, to noble patrons and friends, and even to Philip II, King of Spain. Her preoccupation with material objects spanned this diverse range. This thesis applies a material culture study to the objects mentioned in Teresa’s letters, offering new insights into her experience of the fragility of human embodiment. It analyzes Teresa’s experience with medical remedies, religious textiles, food, and possessions, illuminating both Teresa’s anxiety with the vulnerability of the fragile human body as well as her respect and care for the condition of human embodiment. This attention to Teresa’s concern with embodied materiality nuances our understanding of how early modern nuns navigated the spiritual and material demands placed on them by monastic poverty