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    Investigating the relationship between staffing structures, working conditions, and quality of care in Ontario Long-Term Care Homes
    (University of Waterloo, 2026-09-22) Miller, Michaella
    Background: Chronic underfunding and increased privatization of the Canada’s long-term care (LTC) sector, coupled with an older population and increased complexity of care, has deteriorated working conditions. These conditions have created a reliance on contract work, intensified and stressful work, and decreased job satisfaction and created environments where workers are less inclined to stay at their jobs. Additionally, Ontario mandated an increase in direct care hours required for residents and intends to build more beds for older adults in need of care. These changes require additional staff to meet regulated care requirements. There is a clear need for strategies to recruit and retain a strong LTC workforce and to address retention by improving working conditions. Methods: This thesis sought to explore the relationships between quality of care, working conditions, and staffing practices in LTC through a narrative scoping review, an analysis of staffing data and resident care outcomes, and a qualitative analysis exploring the work experiences of staff and how staffing practices affect care and work. Results: The narrative scoping review (Chapter 2) investigated the contextual factors affecting staffing decisions and the impacts of staffing structures on direct care workers' quality of work-life and work-related outcomes. Contextual factors included market-based ideologies, increased care complexity, regulatory requirements, the impact of COVID-19 and organizational fiscal austerity. Generally, these factors reduced the number and skill mix of staff prior to the COVID-19 pandemic and negatively impacted the quality of work-life and work outcomes, such as reducing job satisfaction and increasing burnout. When homes relied on lean staffing models to save on costs, staff compensated by absorbing the negative impacts of fewer staff by working unpaid overtime and finding workarounds. Chapter 3 examined how PSW staffing patterns were associated with resident care quality outcomes in LTC. The regression analysis of the relationship between PSW staffing and resident quality of care composite score did not find any statistically significant relationships. The lack of relationships in Chapter 3 may be further explained, in part, by the qualitative exploration of staffing in LTC (Chapter 4). This study explored the LTC work environment and staff perceptions of how staffing is organized insofar as it affects their ability to provide care and have a healthy working environment. The qualitative analysis found that the addition of PSWs, as per mandated hours of care regulation, improved workloads for PSWs. However, quality of employees was as important as the quantity of employees. Disruption in team dynamics and distressed care markets may have resulted in a lower quality workforce. In addition, those in supervisory positions in LTC have a workload that has quietly changed and increased in scope but remained unmonitored, resulting in those with the most responsibility in LTC homes struggling with their workloads. Staffing in the LTC organization were not allocated based on care acuity, which impacted whether appropriate care personnel were available for different care requirements and created inequitable workloads between care units in the homes. Conclusions: With the growing demand for LTC globally, there will be a need to train, hire, and employ more care workers. To ensure that the care received by residents is of high quality, sustainable systems will need to be upheld and established that address the conditions of care, including work organization and staffing allocation appropriate to the care needs, education standards that promote well trained hiring pools, appropriate recognition of the time allocated for additional roles and responsibilities, and regulation that addresses skill mix disparities.
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    Safe, Adaptive, and Scalable AI-Driven Energy Management of Urban Multi-Energy Systems
    (University of Waterloo, 2026-09-22) Kenawy, Ahmed Shaban Omar
    Cities serve as central hubs of economic, industrial, and social activity across nations. However, the high population densities, essential infrastructure networks, and diverse industrial operations position cities as the largest energy consumers and the primary contributors to greenhouse gas (GHG) emissions. This poses critical energy and environmental challenges on the urban scale. These challenges include the need to enhance energy generation and consumption efficiency and to achieve a substantial reduction in GHG emissions across different energy sectors. One promising solution is the transition from the traditional, independent planning and operation of energy sectors to the integrated planning and operation of Urban Multi-Energy Systems (UMESs). In this context, a UMES can be defined as a city-scale energy system that coordinates electricity, natural gas, heating, cooling, and other energy sectors as a single integrated system. This integration begins at the multi-energy buildings level, expands to community-level Multi-Energy Microgrids (MEMGs), and ultimately forms city-scale Networked Multi-Energy Microgrids (N-MEMGs) connected through electricity, gas, and heat distribution networks. By coordinating multiple energy carriers, UMESs can facilitate integration of variable renewable energy, improve overall energy efficiency, and enhance operational flexibility and resilience. Despite these advantages, energy management of UMESs is a particularly challenging operational problem. This is due to complex multi-energy flows, uncertainty propagation, decentralized ownership, privacy limitations, and the need for emission-aware operation. In this context, the energy management of UMESs has been addressed using two distinct approaches: model-driven and AI-driven. Model-driven approaches can explicitly represent physical constraints and yield interpretable solutions; however, they often suffer from high computational complexity and rely on complex mathematical optimization models, thereby limiting their suitability for real-time applications. On the other hand, AI-driven approaches offer adaptive decision-making capabilities under uncertainty. Among these approaches, Deep Reinforcement Learning (DRL), which formulates sequential optimization problems as Markov Decision Processes (MDPs), has gained significant attention. However, conventional DRL methods lack explicit mechanisms to enforce physical constraints, thereby limiting their applicability to real-world energy management systems. The primary objective of this thesis is to develop a safe, adaptive, and scalable AI-based energy management framework for UMESs. The proposed framework models the system as a Constrained Markov Decision Process (CMDP) and employs Constrained Deep Reinforcement Learning (CDRL) algorithms to solve it. The framework aims to generate low-carbon and economically efficient dispatch strategies while adhering to the operational and engineering constraints inherent in physical multi-energy networks. Additionally, it accounts for uncertainties related to renewable energy resources, energy prices, and energy demand. The first part of the thesis addresses the energy management problem of single MEMG systems through a hierarchical two-layer framework. In the first layer, a model-driven Mixed-Integer Quadratic Programming (MIQP) formulation performs day-ahead scheduling by minimizing operational costs subject to the relevant system constraints. In the second layer, a CDRL agent performs real-time adjustments to compensate for uncertainty and operational deviations. This framework combines the strength of CDRL in sequential decision-making under stochastic conditions with the structural and feasibility guarantees offered by model-driven mathematical programming. In this way, the day-ahead optimization layer guides the agent’s exploration during training and reduces reliance on black-box policies during real-time operation. Although the model-driven day-ahead layer developed in the first part provides valuable guidance for the CDRL agent’s decisions, it becomes computationally expensive as system complexity increases. Therefore, the second part of the thesis eliminates this dependence and solves the energy management problem for a single MEMG using a fully model-free CDRL approach. This approach incorporates an integrated demand response (IDR) strategy that jointly exploits demand-side flexibility across electricity, heat, and gas, and it models carbon capture, storage, and utilization (CCSU) facilities that enable participation in carbon trading markets (CTMs). The operational dynamics of the system are formulated as CMDP and solved using the interior-point policy optimization (IPO) algorithm. The proposed framework yields dispatch decisions that improve both economic and environmental performance while ensuring satisfaction of system constraints. The final part of the thesis extends the approach to cooperative energy management of networked MEMG systems using a Constrained Multi-Agent DRL (CMADRL) method. Here, the problem is formulated as a constrained Decentralized Partially Observable Markov Decision Process (Dec-POMDP). This jointly captures the interactions between each MEMG operator and the energy network operators, such as power, gas, and heat. The Dec-POMDP balances a cooperative economic-emission objective with physical network constraints. This formulation explicitly accounts for both the internal distributed generators (DGs) within each MEMG and the cross-network coupling DGs. It further defines the necessary components for multi-agent training, including decentralized observations, agent-specific action spaces, and a cooperative reward. In addition, a novel Sensitivity-Weighted Constraint Decomposition (SWCD) mechanism, derived from network sensitivities, is developed to compute per-agent physics-informed constraint-cost signals, enabling the training of multiple agents for safe and cooperative dispatch. CMADRL is trained using the HeterogeneousAgent Proximal Policy Optimization-Lagrangian (HAPPO-Lag) algorithm. The resulting physics-informed framework enforces constraints across the coupled power, gas, and heat distribution networks in a decentralized and privacy-preserving manner. Collectively, these three contributions establish a unified, safe, adaptive, and scalable AI-driven framework that progresses from single MEMG to city-scale networked multienergy systems. The proposed methods deliver low-carbon, economically efficient energy dispatch while ensuring compliance with the physical and engineering constraints governing coupled power, gas, and heat networks. The framework thus offers a practical pathway toward the real-time, privacy-preserving, and emission-aware energy management of future urban multi-energy systems.
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    Interaction-aware scheduling of report generation workloads
    (University of Waterloo, 2010-12-16) Ahmad, Mumtaz; Aboulnaga, Ashraf; Babu, Shivnath; Munagala, Kamesh
    This typical workload in a database system consists of a mix of multiple queries of different types that run concurrently. Interactions among the different queries in a query mix can have a significant impact on database performance. Hence, optimizing database performance requires reasoning about query mixes rather than considering queries individually. Current database systems lack the ability to do such reasoning. We propose a new approach based on planning experiments and statistical modelling to capture the impact of query interactions. Our approach requires no prior assumptions about the internal workings of the database system or the nature and cause of query interactions; making it portable across systems. To demonstrate the potential of modelling and exploiting query interactions, we have developed a novel interaction aware query scheduler for report-generation workloads. Our scheduler, called QShuffler, uses two query scheduling algorithms that leverage models of query interactions. The first algorithm is optimized for workloads where queries are submitted in large batches. The second algorithm targets workloads where queries arrive continuously, and scheduling decisions have to be made online. We report an experimental evaluation of QShuffler using TPC-H workloads running on IBM DB2. The evaluation shows that QShuffler, by modelling and exploiting query interactions, can consistently outperform (up to 4x) query schedulers in current database systems.
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    Orthogonal cartograms with few corners per face
    (University of Waterloo, 2010-12-03) Biedl, Therese; Velazquez, Lesvia Elena Ruiz
    We give an algorithm to create orthogonal drawings of 3-connected 3-regular planar graphs such that each interior face of the graph is drawn with a prescribed area. This algorithm produces a drawing with at most 12 corners per face and four bends per edge, which improves the previous known result of 34 corners per face.
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    Answer set programming or hypercleaning: Where does the magic lie in solving maximum quartet consistency?
    (University of Waterloo, 2010-11-03) Faghih, Fathiyeh; Brown, Daniel G.
    Answer set programming (ASP) approach is an efficient approach for solving the maximum quartet consistency problem. We distinguish two factors that affect the efficiency of a recent ASP approach for solving maximum quartet consistency problem; answer set programming itself and a variety of preprocessing steps. In this paper, we propose a method for applying one of the preprocessing steps used in the ASP approach to an alternative algorithm. Our results show that the preprocessing step gives the main contribution to efficiency of the ASP approach for data sets with less than 20 taxa. We also identify an issue in the ASP approach for solving the maximum quartet consistency problem.