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    Final Report Team Hadati Road: Integrated Structural and Solar Performance Analysis of a Monopitch-to-Flat Roof for a 24-Unit Townhouse Development (located at 140 Hadati Road, Guelph)
    (University of Waterloo, 2026-04-13) Elmaraghy, Omar
    This report presented the final capstone analysis for the project titled Integrated Structural and Solar Performance Analysis of a Monopitch-to-Flat Roof for a 24-Unit Townhouse Development at 140 Hadati Road, Guelph, Ontario. The work investigated how roof geometry affected structural demand, photovoltaic potential, zoning feasibility, and overall project value for a proposed low-rise multi-unit residential development. The central problem was that a conventional roof choice could not be evaluated on appearance alone. On this project, the roof had to satisfy several competing requirements at the same time. It had to remain feasible within the site’s height constraints, perform safely under local snow and wind conditions, support rooftop solar generation, and create value for the owner, Dotty Lake Retreats, without introducing unnecessary detailing risk. The project had compared five different roof forms: a full monopitch roof, a multi-gable roof, a butterfly roof, a flat roof, and a monopitch roof that transitions to flat at the zoning height limit. The project used a comparative engineering design approach supported by preliminary structural calculations, load-path reasoning, photovoltaic sizing, energy-yield estimation, bracing analysis, and an interior-volume ROI assessment. The common basis for comparison was drawn from the project footprint and climate assumptions. This involved a building length of approximately 38.521 m, a span direction of approximately 14.200 m, a roof dead load of 0.65 kPa including PV, Guelph climatic values of ground snow Ss = 1.9 kPa, associated rain Sr = 0.4 kPa, and hourly wind pressure q1/50 = 0.36 kPa. These assumptions were used to generate comparison metrics across different roof options, including baseline snow load, drift-sensitive hotspot load, beam-equivalent factored moment, PV system size, annual energy yield, and bracing demand. The study found that the full monopitch roof delivered the strongest solar and average structural performance, but it was the least compatible with the zoning height constraints. The flat roof was the simplest option from a zoning and massing perspective, but carried the highest baseline snow load and lower solar upside. The multi-gable and butterfly roofs were both disadvantaged by fragmented drainage, more complicated detailing, reduced continuous PV coverage, and more severe localized drift conditions. The monopitch-to-flat roof emerged as the best overall concept because it retained most of the solar and spatial advantages of a sloped roof while respecting the site envelope. This option produced approximately 90.1 kWdc and 99–113 MWh/year, while the preliminary package’s conservative mixed-roof estimate produced approximately 80.4 kWdc and 92,500 kWh/year depending on coverage assumptions. Overall, the recommended direction to my clients and stakeholders was to proceed with the monopitch-to-flat concept as the preferred design, while using a flat roof as the fallback option if simplified permitting, massing, or cost certainty became the dominant consideration.
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    Older but Not Younger Preschoolers Use Efficiency to Evaluate How Well Goals Are Completed
    (American Psychological Association, 2026) Sehl, Claudia G.; Tokat, Nikki; Denison, Stephanie; Friedman, Ori
    Instrumental rationality dictates that goals should be completed efficiently rather than inefficiently. Here, we examined how young children come to use efficiency to evaluate how well goals are completed. Across a series of experiments, we showed children aged 3-7 (total N = 618; 48% female and 52% male; tested in a predominantly White region) scenarios where pairs of agents approached objects using paths that varied in length and efficiency, and then asked children which agent did a better job. In Experiments 1A and 1B, children aged 3 and 4 evaluated the agents using path length, but not efficiency. In Experiments 2A, 2B, and 3, children used path length to assess which agent did a better job from around age 4. But children only used efficiency from age 5 or later. These findings suggest that children's explicit judgments are not guided by the norm for efficiency, and Experiment 4 provides evidence that this late development is not an artifact of methodological problems. This developmental pattern might arise from limits in children's ability to make counterfactual judgments about what agents could have done.
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    Field-theoretic simulations for block copolymer melts using the partial saddle-point approximation
    (MDPI, 2021) Matsen, Mark W.; Beardsley, Thomas M.
    Field-theoretic simulations (FTS) provide an efficient technique for investigating fluctuation effects in block copolymer melts with numerous advantages over traditional particle-based simulations. For systems involving two components (i.e., A and B), the field-based Hamiltonian, H_f[W_-,W_+], depends on a composition field, W_-(r), that controls the segregation of the unlike components and a pressure field, W_+(r), that enforces incompressibility. This review introduces researchers to a promising variant of FTS, in which W_-(r) fluctuates while W_+(r) tracks its mean-field value. The method is described in detail for melts of AB diblock copolymer, covering its theoretical foundation through to its numerical implementation. We then illustrate its application for neat AB diblock copolymer melts, as well as ternary blends of AB diblock copolymer with its A- and B-type parent homopolymers. The review concludes by discussing the future outlook. To help researchers to adopt the method, open-source code is provided in the Supplementary Materials that can be run on either central processing units (CPUs) or graphics processing units (GPUs).
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    Critical point of symmetric binary homopolymer blends
    (2016) Spencer, Russell K. W.; Matsen, Mark W.
    Monte Carlo field-theoretic simulations (MC-FTS) are performed on structurally symmetric binary homopolymer blends for invariant polymerization indexes of N > 10^3. It is shown that the ultraviolet (UV) divergence that plagues MC-FTS at small N can be removed by an appropriate renormalization of the Flory-Huggins interaction parameter, chi, allowing one to extract meaningful results that are independent of the wavevector cutoff. Once the divergence is taken care of, the fluctuation corrections to mean-field theory are found to be exceptionally small. In particular, the disordered-state structure function, S(k), is virtually indistinguishable from the RPA prediction, and there is a slight shift in the critical point, (chi N)_c, that roughly scales as N^-0.5. An implication of the small corrections is that previous experimental determinations of chi based on homopolymer blends should be relatively accurate.
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    Evaluating and Improving Generative Models with Feedback
    (University of Waterloo, 2026-11-05) Jiang, Dongfu
    Generative models can produce many candidate outputs for a request, but deciding which output is good, and what a model should learn next, requires feedback, a judgment of how well an output serves its task. This thesis studies how to obtain such feedback and how to use it, for evaluating generated outputs and for improving the models that produce them. The two uses place different demands on feedback. For evaluation, feedback has to agree with human judgment and explain its judgment. For training, it has to stay reliable while a model is optimized against it. The first part studies feedback for evaluation. For text, I show that feedback can be learned without a reference output, by training an evaluator on error analyses so that it locates and explains each error in a response, and its scores agree with human ratings nearly as well as GPT-4 used as a judge. For images and videos, I collect pairwise human votes on an open platform, use them to rank generative models, and release them as a benchmark on which the best multimodal judge tested agrees with people about half the time, so human preferences remain the reference for this content. The second part studies feedback for improving models, where agreement with people on fixed outputs is not enough. A policy trained against the learned text evaluator learned to refuse rather than answer, raising its reward while its accuracy fell. For code, I synthesize tests for tens of thousands of programming problems and turn execution results into preferences and rewards, which improve both Best-of-N selection and reinforcement learning. For agents that use tools over several turns, I build a reinforcement-learning framework that runs tools inside the training loop, train agents in six domains, and examine two choices in the feedback they receive. Rewarding tool use raises tool calls without necessarily raising task performance, and how much of a tool's output the policy reads can change the result. Across these studies, feedback that evaluates fixed outputs well can behave differently once a model is trained against it, so a training reward needs a measure it does not see, tracked while the model is optimized.