Analysis of the Economic and Carbon Emission Reduction Potential of Fuel Cell Electric Vehicle-to-Grid in Alberta and Ontario

Connecting battery electric vehicle (BEV) to the grid is a way of utilizing existing BEV fleet to cut the cost on energy storage and provide monetary incentives to vehicle owners. By coordinating the charging and discharging of the growing BEV fleet, the grid load can be shifted. Meanwhile, fuel cell electric vehicles (FCEVs) are gaining popularity, especially in heavy-duty vehicle market because of the advantages of hydrogen over battery such as the higher gravimetric density and faster refueling time. Similarly, FCEV fleet can also be connected with the grid (FCEV2G) and become moving energy generators that generate electricity and supply it to the grid using hydrogen. The hydrogen used can be produced locally with cheap and excess electricity or in a centralized production site at lower cost. A profit could be made to benefit from the high electricity price during peak hours, which can be shared among FCEV owners and the FCEV2G coordinator. This study analyzes an FCEV2G station that can connect a few FCEVs to the grid to generate electricity. The operation, including local hydrogen production and storage, hydrogen purchased from a centralized plant, and schedules of FCEV2G, is modeled as a mixed integer linear programming problem. Using historical data of electricity price and generation mix in Alberta and Ontario, in 2019 and 2022, The profits of this FCEV2G station with different configurations are optimized and compared. Parameters including component efficiency, onsite electrolyzer are studied to investigate their impacts on the optimization result. The carbon emission potential of FCEV2G is also evaluated. The results in Alberta show that an annual net revenue as high as 66k USD could be made in 2022 via FCEV2G, as the high and volatile electricity prices amplify the load-balancing function of FCEV2G. In addition, 185 t CO2 emission could also be avoided by using clean hydrogen to generate electricity and supply it to the carbon-intensive grid in Alberta. However, under the base case assumption, such a FCEV2G station could not make profit in 2019 in Alberta because of the efficiency losses of the electrolyzer and fuel cells as well as the relatively stable electricity price. This means, high and unstable electricity prices through a year are the key factors for FCEV2G to be profitable. On the other hand, Ontario has abundant nuclear and hydro power supply and hence maintain a stable electricity price profile. A parametric study is conducted to study how the profitability will depend on technological improvements in the future, and it finds that, by using the 2022 data, the FCEV2G station becomes profitable after market hydrogen cost divided by fuel cell efficiency is below 86 USD/MWh. Meanwhile, the carbon intensity of electricity varies largely in Ontario because natural gas is primarily used to meet peak demands. This allows a FCEV2G pathway to reduce the carbon emissions during peak hours, and the result shows as high as 213 t CO2 emissions could be reduced in the 2022 base case

The World of My Childhood Home

My childhood home ceased to exist and remained in the past. This thesis is a return to my childhood home through my memories. I return to it in remembering my child-self, who sought the heights from the roof and high platforms, the child who dug out the earth and mother’s wardrobe, the one who feared the drainage holes in the kitchen and bathroom, and the child who desired the fire in the living room and the tall dark shaft. it is a return to the world of my childhood home in writing, drawing, rewriting, then redrawing. My memories of the house are literary imagination in architecture

An Investigation of Human Annotators' AI Teammate Selection and Compliance Behaviours

Human-artificial intelligence (AI) collaborative annotation has gained increasing prominence as a result of its enormous potential to complement human and AI strengths as well as AI's recent development. However, it is not straightforward to form suitable human-AI teams and design human-AI interaction mechanisms for effective collaborative annotation. Through an exploratory study, this thesis investigated a diverse set of factors that may influence humans' AI teammate selection and compliance behaviours in a collaborative annotation context wherein AI agents serve as suggesters to humans. The study results indicate that multiple factors influenced which AI agents the participants chose to receive suggestions from, such as the AI agents' recent and overall accuracies as well as the participants' suggestion compliance records. We also discovered that the participants' AI compliance decisions were swayed by factors including whether the AI agents' suggestions aligned with the participants' top choices and whether such suggestions provided novel perspectives to the participants. Moreover, it was found that most of the participants constructed narratives to interpret the differences in various AI teammates' behaviours based on limited evidence. This thesis also contributes by presenting MIA, a versatile web platform for mixed-initiative annotation. Based on the weaknesses of MIA's current designs, as informed by empirical results of the aforementioned exploratory study and another human-AI collaborative annotation study, as well as the goals to improve MIA's scalability and adaptability, this thesis proposes design changes to MIA; these design changes also apply to other mixed-initiative annotation platforms

Towards Photonic Chip Integration of an Oscillating Photonic Bell State from a Semiconductor Quantum Dot

The demand for entangled photon sources has been growing in all areas of quantum information technologies. However, many modern single-photon sources suffer from fundamental limitations which limit their ability to produce these single-photons, as many of the “high quality” sources are probabilistic in nature. This probabilistic emission limits the “on-demand” requirement needed for many quantum information technologies. As a result, other types of single photon sources have been the topic of research for the past two decades, in which quantum dots are a promising candidate. Quantum dots are regions in space which electron-hole pairs can be confined and, using a phenomenon called ”spontaneous emission,” can emit single photons on demand. This is because the creation of electron-hole pairs is a deterministic process, which is a major topic of study in this thesis. In addition, the quantum dot which is the focus of this thesis is capable of emitting pairs of entangled photons on-demand, which have a wide array of applications in the quantum computing community. Our quantum dot is also embedded inside a nanowire, which this thesis will show, improves its performance in the areas which are considered important to the quantum dot, quantum computing, quantum networking, and quantum communication communities. The nanowire quantum dots, comprised of an InAsP quantum dot embedded in an InP nanowire studied in this thesis show improvements over other quantum dot sources in key areas: photon purity, with a g(2)_XX (0) = 0.0055 ± 0.0003 and g(2)_X (0) = 0.0028 ± 0.0003, and entanglement, with peak concurrence (fidelity) values of C = 95.6 ± 0.7% (F = 97.7 ± 0.4%) and lifetime weighted concurrence (fidelity) of C = 90.2 ± 0.2% (F = 94.0 ± 0.1%). These results are an improvement from previous results on the same nanowire quantum dot, with peak concurrence of C = 87 ± 4% and lifetime weighted concurrence of C = 52 ± 3%. The reason for the improvements on the same dot is because of the detection systems, superconducting nanowire single photon detectors. These detectors have far less timing jitter, dark counts, and higher efficiency than their counterparts, single photon avalanche diodes, which suggests these quantum dots are far better than previously thought. This thesis explores the effects of the detection systems in resolving a time-dependent oscillating quantum state, which occurs when a quantum dot has a fine-structure splitting. The fine-structure splitting is present in quantum dots that have strain, random alloying, or an asymmetric confining potential, causing asymmetry in the physical dimensions of the quantum dot. This asymmetry causes a lifting in the degeneracy of the exciton state, which causes the exciton state to precess. This oscillation happens on a timescale similar to the single photon avalanche diodes timing jitter, causing a “blurring” of the oscillating quantum state. This is mediated by using faster detectors, which this thesis will explore. In addition to single photon sources, this thesis also focuses on the creation of photonic integrated circuits capable of supporting the photons emitted from our quantum dot. This is important because our quantum dot emits at wavelengths ≈ 894 nm, which is not supported by the silicon platform typically offered for photonic integrated circuits. This thesis will focus on a new platform, silicon nitride, which is capable of supporting light at our wavelengths. The results of this thesis demonstrate the simulated performance of the silicon nitride photonic integrated circuits at our wavelengths and lay the groundwork for a path forward to testing them with our source

Modelling effects of stormwater best management practices on urban stormwater runoff phosphorus

Phosphorus (P) is a key limiting nutrient for algal growth in freshwater whose excess loading to freshwater bodies contributes to cultural eutrophication and the associated symptoms of water quality deterioration. Urban stormwater is a significant contributor of P to downstream ecosystems from various point and non-point sources and via a variety of transport and emission pathways. Stormwater best management practices (BMPs) such as stormwater ponds (SWPs, a type of traditional stormwater BMP) and bioretention cells (BRCs, a type of low-impact development (LID) BMP) have the potential to attenuate P loads from urban areas and hence mitigate eutrophication risks to aquatic ecosystems. Despite their rapidly growing implementation worldwide, the effects of these stormwater BMPs on urban stormwater P concentrations and loads remain poorly understood. In this thesis, I assess the effects of urban stormwater BMPs on P export, with the goal of determining (1) what are the knowns and unknowns regarding the sources, pathways, and influence of stormwater BMPs on urban P export, (2) what are the dominant internal processes that control P reduction in BRC, based on process-based modelling, (3) what are the general effects of BRCs on urban stormwater runoff P and how are they different from the effects on nitrogen (N), and (4) how to predict the effects of BMPs on urban stormwater runoff P through the use of data-driven models, and what are the potential influencing factors. I address these research questions by reviewing urban P sources discussed in the literature, quantifying P mass balance in a BRC facility in Mississauga, ON, assessing effects of urban stormwater BMPs on P export based on data from the International Stormwater BMP Database, and through the development of process-based and data-driven BMP P models. In Chapter 2, I review the existing literature and analyze data from the International Stormwater BMP Database (ISBD) to summarize the sources, pathways and speciation of urban stormwater P, and the effects of urban stormwater BMPs on P export. This study acts as an introduction to the issues of P in urban stormwater runoff and identifies the research gaps associated with understanding effects of stormwater BMPs on urban stormwater P export. I show, based on both previous literature and the data in the ISBD, that the effects of stormwater BMPs on urban P export remain highly uncertain and unknown. There is a lack of predictive tools for estimating effects of stormwater BMPs on urban P export, and I go on to fill this research gap in Chapters 3, 4, and 5. Following Chapter 2, I address my research questions by developing a process-based P model for a BRC facility in Mississauga, ON. This model is calibrated using field monitored data for flow, water quality and filter media soil chemistry (from core samples). In Chapter 3, the model simulates the multi-year P partitioning, accumulation and export in this stormwater BMP. I show, via the analysis of model simulation results, that exfiltration to underlying native soil was principally responsible for decreasing the surface water discharge from the BRC (63% runoff reduction), while accumulation in the filter media layer was the predominant mechanism responsible for the reduction in P outflow loading (57% retention of total P (TP) inflow load). Of the P retained within the filter media layer, only 11% was stored in easily mobilizable forms. There were no signs that the P retention capacity of the BRC was approaching saturation after 7 years of operation. Thus, my results demonstrate sustained efficient P load reduction by this BRC. In Chapter 4 I evaluate the general effects of BRCs on urban stormwater runoff P concentration and loading by analyzing data from a large number of BRCs in the ISBD from across the United States. I further compare the influence of BRCs on P and N export. I also introduce the data-driven approach in Chapter 4 by training a random forest model to predict the reduction and enrichment effects of BRCs and compare the importance of different explanatory variables. I show that while BRCs typically enrich concentrations of TP and soluble reactive P (SRP), the corresponding outflow loads of TP and SRP, were generally lower, mainly because of reductions to surface runoff volumes via exfiltration to the subsurface. This finding raises questions regarding the relative importance of this infiltrating P to the subsurface environment and potential impacts to groundwater quality. Because they are generally more efficient in reducing N loads than P loads, BRCs tended to decrease the N:P ratio of stormwater runoff, potentially altering nutrient limitation patterns in receiving aquatic ecosystems. My findings also imply that the impacts of BRCs on P and N concentrations, speciation, and loads in urban runoff are highly variable. This variability can be partly accounted for by some explanatory variables related to the climate, watershed and BRC characteristics, and predicted by machine learning (ML) methods such as the random forest model. Random forest modeling identified inflow concentrations and BRC age as key variables modulating the changes in TP, SRP, and total N concentrations between inflow and outflow. For dissolved inorganic N, the BRC’s storage volume and drainage area also emerged as important explanatory variables. Chapter 5 also focuses on the ISBD, similar to Chapter 4, but the analysis of P control performance is expanded to six categories of BMPs. I compare the accuracy of different data-driven models for the prediction of BMP P reduction/enrichment factors, through the use of different ML methods. I show that although LID BMPs are generally more efficient at reducing runoff quantity, they are more likely to enrich TP and SRP concentrations compared to traditional BMPs leading to poorer P load reduction performance amongst LID BMPs. Both traditional and LID BMPs are more likely to enrich SRP concentration when influent SRP concentration is low, in watersheds with higher imperviousness and in drier climates. The influence of LID BMPs on SRP concentration is also more sensitive to climate, watershed and BMP characteristics compared to traditional BMPs. I show that the random forest model provides the most accurate estimation of BMPs effects on urban stormwater P concentrations when compared to models produced using other ML methods. This study suggests that switching to LID BMPs has the potential to increase eutrophication risks and requires further examination. It also proposes that ML methods, especially use of the random forest model can represent a more robust approach to estimate the effects of stormwater BMPs on urban runoff P by accounting for both P reduction and enrichment effects. My results show that that BRCs and other stormwater BMPs have highly variable effects on urban P export. I show that although the BRC I investigated in Mississauga, ON, exhibits efficient reduction of P export, it appears to be atypical and that BRCs and other LID BMPs are generally more likely to enrich P concentration compared to traditional BMPs based on data from a large number of BMP systems in the ISBD. This concentration enrichment may further impact the quality of groundwater and surface waterbodies. Considering the global environmental policy trend to promote replacement of traditional stormwater BMPs with LID BMPs, the findings of this thesis should serve as a caution to policy makers, as understanding of the effects of stormwater BMPs on urban P export remain incomplete

Re-Imagining Healthy Aging within an Intergenerational Community

There has been a demographic shift resulting in an increasing number of older people than younger people in the demographic pyramid. As a result, there is a strain on the number of resources available, such as senior specific homes, to accommodate the growing need for housing the elderly of our society. In addition, with ever-increasing prices in the real-estate market, general housing shortages, and the lack of affordable housing, low-income seniors have limited choices, in many cases, none. Moreover, the central city as a place has today come to typically cater towards the younger generation, and especially young couples. Many families with children and older people choose to relocate to the suburbs. There are, however, more sustainable solutions as the present situation requires an overly heavy reliance on cars due to the spread of many amenities and resources in a low-density urban fabric. This thesis emphasizes a push toward more generationally shared living within the central city urban context. Options for some like the elderly are limited because in today’s real estate market they can’t find adequate housing to allow for healthy aging, so they are often forced to turn to senior-specific housing resulting in them being physically and socially isolated. Similarly, cities lack the space and shared affordability needed for families to grow in the urban context. Multi-generational housing set as a community hub is a potentially viable alternative choice to the current care models and existing housing. The aim of the thesis is to answer the question: how can older adults and their families fit into the urban context, and how can the young and old generations co-exist in a shared residential space? The thesis analyzes different design and planning strategies to create an inclusive care community that supports continual aging. A design framework and strategies will be developed to propose an intergenerational community that supports healthy aging. This approach will be developed as a conceptual urban and architectural design that explores ways to facilitate care between all generations and create a shared space in the urban context of Toronto, one that encourages aging in place and social interaction between a diverse aging population and younger generations. The thesis outcome is an architectural community project that re-imagines healthcare, residential and mixed-use urban and building developments for every stage of life

Joint modeling, variable selection and multiply robust estimation in mediation analysis with multiple mediators

This thesis explores topics in causal mediation analysis with multiple possibly related mediators. The goal of this thesis is to propose innovative methodologies for joint modeling of multiple uncausally related mediators, selecting mediators from high-dimensional candidates while simplifying their dependency structures and performing multiply robust estimations to uncover causal effects of interest. Causal mediation analysis aims to enhance understanding of the effects of an exposure on an outcome by examining direct and indirect effects. In settings where multiple mediators are involved, the relations among these mediators play an important role. Traditional studies focus on the scenario that the multiple mediators are either related under specified causal structures or independent given baseline covariates. Our studies focus on multiple uncausally related mediators, where the mediators are associated with each other conditioning on pre-treatment covariates and treatment but there is no causal ordering among them. In Chapter 2, we begin by reviewing and expanding upon the concept of mediators that are uncausally related, followed by the introduction of causal effects defined under such settings and the associated identification assumptions. We propose to jointly model the uncausally related mediators using copula functions. An important advantage of employing copula functions in joint modeling is the significant flexibility it offers, as this method allows for multiple mediators to have different distributions and be correlated in various ways. Subsequently, we propose methods estimating causal effects within this framework. In Chapter 3, we center our attention on the sparse mediation phenomenon, where only a handful of true mediators, from a pool of possibly high-dimensional candidates, exhibit nonzero indirect effects. We propose a LASSO-based penalization technique that selects the true mediators by considering their indirect effects. Acknowledging that the selected mediators often still exhibit complex dependency structures even after selection, our method also simplifies these structures by selecting non-zero correlation entries within the correlation matrix using a similar penalized estimation technique. To facilitate the correlation structure selection, we transform the correlation matrix selection problem into a standard variable selection problem within the framework of a linear model. Moreover, our proposed method allows the mediator selection and the dependency structure selection processes, to be conducted either via either a parallel or a sequential approach. The grouped and individual causal effects are defined under such settings with estimation approaches discussed. In Chapter 4, we discuss the issue of model misspecification within the context of causal mediation analysis. Following the discussion, we propose two ways of constructing multiply robust estimators. In causal mediation analysis, typically three working models must be specified: the treatment model, the mediator model, and the response model. Both of our multiply robust estimation methods yield consistent estimation of the causal quantities of interest, provided that any two out of the three models are correctly specified. For each proposed method introduced in Chapters 2, 3 and 4, we provide theoretical results with proofs of the consistency and other properties. We also derive large sample properties and investigate finite sample properties via simulations. Each chapter includes an application of the proposed method to a genetic study in psychiatry to investigate DNA methylation loci as mediators on the causal path between childhood trauma and stress reactivity. In Chapter 2, the proposed method estimates the mediation effects of three DNA loci on the Kit ligand gene. Chapter 3 extends this analysis and applies the proposed mediator selection method to the entire DNA methylation dataset, revealing 12 mediating loci, with 10 showing a strong association. We estimate the grouped indirect effect from them and the individual effects of the remaining two loci. In Chapter 4, we employ our multiply robust estimation methods to re-evaluate the mediation effects of these 12 loci, demonstrating enhanced robustness to previous findings

Hunting for the Primordial Quenched Population in 2 < z < 2.5 COSMOS Protoclusters

We present an analysis of the galaxy stellar mass function (SMF) of 14 known protoclusters between 2.0 10¹⁰ᐧ⁵ M⊙) quiescent galaxies by a factor ≳ 2. However, we find that at lower masses (M⋆ < 10¹⁰ᐧ⁵ M⊙), no additional environmental quenching is required

Securing Vehicular Networks: A Rules-Based CAN Intrusion Detection System Using IoT Edge Architecture

The increasing interconnectivity of modern safety-critical embedded systems has led to an ever-increasing attack surface. The automotive and maritime industries are but two industries that use safety-critical embedded systems. A common protocol used in both industries is the Controller Area Network (CAN) protocol, which has been proven to have multiple security flaws. This thesis proposes a novel rules-based CAN Intrusion Detection System (IDS) to protect against possible attacks via the CAN protocol and alert end users in real-time. A rules-based approach was chosen due to the ability to dynamically adapt to the varying state of CAN messages. Previous rules-based implementations use a small number of rules, leading to the potential to misclassify incoming CAN messages. This thesis expands on previous implementations by proposing 16 established rules in total. The proposed rules-based CAN IDS leverages an IoT (Internet of Things) architecture to provide centralised management of the IDS and to give the capability of deploying the IDS at scale. This thesis tests the proposed rules-based CAN IDS on two real-world systems that use the J1939 and NMEA 2000 protocols, with the primary testing performed on a 2016 Peterbilt 579 truck. Interesting observations from testing the rules-based CAN IDS found that manufacturers do not follow J1939 standards and a five-millisecond per message limitation in the Azure IoT Edge infrastructure

Bipartite graphs with no K6 minor

This publication is available at Elsevier via https://doi.org/10.1016/j.jctb.2023.08.005 © 2023 The Author(s). Published by Elsevier Inc. This is an open access article under the CC-BY license (http://creativecommons.org/licenses/by/4.0/).A theorem of Mader shows that every graph with average degree at least eight has a K6 minor, and this is false if we replace eight by any smaller constant. Replacing average degree by minimum degree seems to make little difference: we do not know whether all graphs with minimum degree at least seven have K6 minors, but minimum degree six is certainly not enough. For every ε > 0 there are arbitrarily large graphs with average degree at least 8 − ε and minimum degree at least six, with no K6 minor. But what if we restrict ourselves to bipartite graphs? The first statement remains true: for every ε > 0 there are arbitrarily large bipartite graphs with average degree at least 8 − ε and no K6 minor. But surprisingly, going to minimum degree now makes a significant difference. We will show that every bipartite graph with minimum degree at least six has a K6 minor. Indeed, it is enough that every vertex in the larger part of the bipartition has degree at least six.NSF DMS-EPSRC, DMS-2120644 || EPSRC, EP/V007327/1 || NSF, DMS-2154169 || AFOSR, A9550-19-1-0187 || NSERC, RGPIN-2020-03912