21090 research outputs found
Sort by
Towards Decision Support and Automation for Safety Critical Ultrasonic Nondestructive Evaluation Data Analysis
No full textA set of machine learning techniques that provide decision support and
automation to the analysis of data taken during ultrasonic non-destructive
evaluation of Canada Deuterium Uranium reactor pressure tubes is proposed.
Data analysis is carried out primarily to identify and characterizes the
geometry of flaws or defects on the pressure tube inner diameter surface.
A baseline approach utilizing a variational auto-encoder ranks data by
likelihood and performs analysis using Nominal Profiling (NPROF), a novel
technique that characterize the very likely nominal component of the dataset
and determines variance from it. While effective, the baseline method
expresses limitations, including sensitivity to outliers, challenged explainability,
and the absence of a strong fault diagnosis and error remediation mechanism.
To address these shortcomings, Diffusion Partition Consensus (DiffPaC),
a novel method integrating Conditional Score-Based Diffusion with Savitzky-Golay
Filters, is proposed. The approach includes a mechanism for outlier removal
during training that reliably improves model performance. It also features
strong explainability and, with a human in the loop, mechanisms for fault
diagnosis and error correction. These features advance applicability in
safety-critical contexts such as nuclear nondestructive evaluation.
Methods are integrated and scaled to provide:
(a) a principled probabilistic performance model,
(b) enhanced explainability through interpretable outputs,
(c) fault diagnosis and error correction with a human-in-the-loop,
(e) independence from dataset curation and out-of-distribution generalization
(f) strong preliminary results that meet accuracy requirements on dimensional
estimates as specified by the regulator in \cite{cog2008inspection}.
Though not directly comparable, the integrated set of methods makes many
qualitative improvements upon prior work, which is largely based on discriminative
methods or heuristics. And whose results rely on data annotation, pre-processing,
parameter selection, and out of distribution generalization. In regard
to these, the integrated set of fully learned data driven methods may be
considered state of the art for applications in this niche context.
The probabilistic model, and corroborating results, imply a principled
basis underlying model behaviors and provide a means to interface with
regulatory bodies seeking some justification for usage of novel methods
in safety critical contexts. The process is largely autonomous, but may
include a human in the loop for fail-safe analysis. The integrated methods
make a significant step forward in applying machine learning in this safety-critical
context. And provide a state-of-the-art proof of concept, or minimum viable
product, upon which a new and fully refactored process for utility owner
operators may be developed
A Comprehensive Process for Addressing Market Power in Decentralized ADN Electricity Markets
No full textElectric power systems have transformed globally, with distribution grids evolving into active distribution networks (ADNs), altering their characteristics and operations. Traditional centralized market structures have become inadequate for the complexities of the ADNs, leading to inefficiencies and challenges in reliable operation and energy pricing. ADN electricity markets offer a solution by leveraging smart grid features to integrate distributed energy resources (DERs), allowing non-utility entities, such as producers, consumers and prosumers, to participate directly, enhancing market efficiency, reducing monopoly power, and limiting utility control over prices. However, with the increasing penetration of DERs, there is a growing risk of market concentration and manipulation by entities owning large shares of DERs in ADN electricity markets. This poses a potential threat to market fairness, as some participants may exploit market power, leading to an uneven playing field, reducing the integrity and efficiency of ADN electricity markets.
From this standpoint, this thesis investigates and adapts the concept of market power within ADN electricity markets, considering the unique characteristics of the market and the system. The investigation is structured around six central questions: (1) Can non-utility entities exercise market power in ADN electricity markets? (2) Is there a comprehensive framework for accurately monitoring, evaluating, and mitigating market power in decentralized ADN markets? (3) If such a framework exists, can it manage the complexity of monitoring the large number of ADN market participants? (4) If market power manipulation exists, are current investigations adequate, considering the decentralized market structure, the physical characteristics of the system, DER operational constraints, and the interplay between active and reactive power markets? (5) What types of decentralized market structures and frameworks—such as fully decentralized, community-based, or network-based peer-to-peer (P2P)—are appropriate for addressing market power in ADN electricity markets? (6) Are traditional market power mitigation methods applicable and effective in the context of ADN electricity markets considering the decentralized nature of the ADN and the dispersed DERs?.
The primary objective of this thesis is to develop a fair and decentralized energy trading platform that limits monopoly power and mitigates market power abuse in ADN electricity markets. To achieve this goal, the thesis proposes an innovative comprehensive process for monitoring, evaluating, and mitigating market power, specially designed for the decentralized structure of ADNs and their market frameworks. This process considers the shifts in network configuration as well as the physical and operational characteristics of ADNs and their components. The process begins by monitoring market power of dominant market participants through introducing the zoning concept. These operational zones narrow down the number of market participants within each zone, addressing the challenge of monitoring a large number of market participants with widely distributed DERs and improving the identification and control of potential market power exercisers, thus minimizing their potential market power. These operational zones serve as decentralized interfaces between the zonal market participants and their corresponding zonal market operators, establishing a decentralized platform for energy trading.
The second stage of the process focuses on evaluating market power through investigating and analyzing the strategic offering behavior of the potential market power exercisers identified in stage one. This analysis is conducted within the framework of a community-based P2P decentralized ADN electricity market, considering the physical and operational characteristics of both the system and DERs, along with the coupled active and reactive power markets. A comparative evaluation of market outcomes under competitive and strategic conditions is performed to identify strategic manipulators. In this context, the study also examines the applicability and effectiveness of conventional market power mitigation techniques used for the centralized market and assesses their impact on the strategic offering behavior of identified manipulators. While some traditional market power mitigation techniques may demonstrate efficiency, a new approach is necessary to address the unique decentralization characteristic of ADN electricity markets.
A novel market power mitigation technique is proposed in the third stage of the process, targeting the root cause of market power: market concentration. This approach introduces an innovative market zoning concept, dynamically partitioning the system into "Market-Zones" to reduce market concentration while adapting to different system operational conditions, considering the uncertainties in system demand and generation, thereby aligning with the decentralized nature of ADNs and their markets. The proposed innovative zoning approach offers a robust solution for mitigating market power in decentralized ADN electricity markets. Within these Market-Zones, each player can actively engage and participate in the market and obtain the benefit without being overtaken by entities with large market shares. Consequently, the market power of the dominant players is subsided and diluted by utilizing the proposed Market-Zones, establishing a fair energy trading platform
Strain engineering and bioprocess development for bio-based production of porphyrins
No full textBio-based production using microbial cell factories has emerged as a transformative approach to addressing the limitations of petrochemical processes, offering renewable, sustainable, and environmentally friendly alternatives for manufacturing valuable chemical compounds. Among various microbial systems, Escherichia coli (E. coli) has become a popular and versatile host for biomanufacturing due to its rapid growth, genetic tractability, and extensive history of industrial use. Through advances in synthetic biology, genome engineering, and metabolic engineering, E. coli can be tailored to produce a wide array of chemicals, including structurally complex compounds like porphyrins. Porphyrins and their derivatives, such as heme and chlorophyll, are critical for various biological and industrial applications, ranging from pharmaceuticals and diagnostics to renewable energy solutions. Despite their significance, challenges such as pathway bottlenecks, feedback inhibition, and intracellular toxicity of intermediates hinder microbial production of porphyrins. This thesis addresses these challenges by implementing integrated engineering strategies to enhance porphyrin biosynthesis in E. coli and establish a robust framework for scalable production.
A foundation of this work was the development of a genome engineering toolkit that integrates CRISPR-Cas9 and transposon-based methods. This system allowed for site-specific insertion of heterologous genes and precise inactivation of endogenous genes, achieving editing efficiencies exceeding 90%. Such flexible manipulation of the E. coli genome facilitated the construction of optimized strains for biomanufacturing. For example, the toolkit enabled the creation of a plasmid-free strain capable of producing polyhydroxyalkanoates, demonstrating its potential for industrial applications and providing the foundation for metabolic engineering efforts targeting porphyrin biosynthesis.
For the subsequent part of our thesis, the biosynthesis of uroporphyrin (UP), a key precursor for heme, was enhanced by implementing the Shemin/C4 pathway in E. coli. Strategies to increase intracellular succinyl-CoA availability and express a synthetic operon containing genes such as hemA, hemB, hemC, and hemD led to UP titers of 901.9 mg/L under batch bioreactor conditions. Furthermore, most of the UP produced was secreted extracellularly, simplifying downstream purification and demonstrating the feasibility of large-scale production. These advancements highlight the effectiveness of pathway optimization in overcoming metabolic bottlenecks.
We used the information obtained from previous chapter to enhance coproporphyrin (CP) biosynthesis. Dual synthetic operons controlled by strong promoters regulated key pathway genes, including hemA, hemB, hemD, hemE, and hemY. Bioreactor cultivation of the engineered strains using glycerol as the primary carbon source under aerobic conditions led to CP titers of up to 353 mg/L with minimal byproduct formation. To the best of our knowledge this study marked the first targeted bio-based production of CP in E. coli, laying the groundwork for its industrial-scale synthesis and emphasizing the importance of precise gene regulation in pathway optimization.
Addressing the complexities of heme biosynthesis required a novel two-step strategy integrating in vivo and in vitro approaches. Engineered E. coli strains expressing the coproporphyrin-dependent (CPD) pathway produced ∼85 mg/L of coproheme and ∼18 mg/L of heme in vivo. However, intracellular heme accumulation posed significant toxicity challenges due to limited secretion into the extracellular medium. These challenges were mitigated by developing an optimized in vitro enzymatic conversion process, achieving a 77.2% reaction yield for the conversion of coproporphyrin III to coproheme and a 45.8% yield for the conversion of coproheme to heme. This integrated approach bypassed intracellular toxicity, enabling controlled and scalable production while addressing key bottlenecks in microbial production systems.
In our final study, to further enhance porphyrin biosynthesis, strategies were developed to mitigate reactive oxygen species (ROS)-induced stress and redirect dissimilated carbon flux toward type-III porphyrin biosynthesis. Antioxidant supplementation with ascorbic acid (up to 1 g/L) improved the UP-III/UP-I ratio from 0.62 to 2.57, enhancing the production of type-III porphyrins. Additionally, overexpression of ROS-scavenging genes such as sodA and kat significantly increased porphyrin yields. Notably, overexpression of sodA alone resulted in a 72.9% increase in total porphyrin production, reaching titers of 1.56 g/L, while improving the UP-III/UP-I ratio to 1.94. These findings underscore the importance of addressing oxidative stress to optimize metabolic fluxes and enhance type-III porphyrin biosynthesis in E. coli. The study provides a practical platform for improving bio-based porphyrin production at industrial scales.
Taken together, this thesis demonstrates the potential of integrating strain engineering, synthetic biology, and metabolic engineering to enhance porphyrin biosynthesis in E. coli. The innovative strategies developed provide scalable, sustainable, and economically viable solutions for producing porphyrins and their derivatives. These advancements open new avenues for industrial applications in pharmaceuticals, diagnostics, and renewable energy, establishing E. coli as a powerful platform for biomanufacturing complex biomolecules
Trajectories of Psychopathology and Mental Health Service Use Among Youth with A Physical Illness
No full textYouth with chronic physical illness (CPI) are at an increased risk of developing co-occurring mental disorders (i.e., multimorbidity). About 40% of youth with CPI receive a mental disorder diagnosis. The relatively high multimorbidity prevalence suggests this is a pressing public health issue—youth with multimorbidity experience greater symptom severity and functional impairment. In addition, multimorbidity negatively impacts psychosocial outcomes, including health-related quality of life, self-esteem, and academic functioning. Although these children experience poorer outcomes and use a greater amount of mental health services, there is limited understanding of how specific trajectories of mental disorder symptoms (i.e., psychopathology) influence service use over time. By examining the course of psychopathology and its association with mental health service use (MHSU), we can identify critical intervention points aiming to improve the effectiveness of care and allocation of resources.
In Canada, the financial burden of mental illness is approximately $51 billion annually, including costs arising from healthcare services, lost productivity, and poor quality of life. Compounding on these burdens, there is an acute shortage of youth-specific mental health services in Canada, where up to 70% of youth with mental health concerns do not receive the specialized services they need. Understanding MHSU in youth with CPI may help efficiently use resources, identify unmet needs, improve the timing of interventions, support families in predicting care needs, and inform policies for targeted and integrated services.
This dissertation addresses critical gaps in research on youth psychopathology among you with CPI. While previous studies have explored youth MHSU, few have examined the unique trajectories of psychopathology in this population or how these trajectories affect mental health service use over time. Additionally, limited research has investigated the interplay between parent psychological distress, youth psychopathology, and healthcare use, particularly using longitudinal models. Focusing on these areas, this dissertation provides insights into the complex needs of youth with CPI. It highlights opportunities for improving integrated mental health and healthcare support for this at-risk group.
To address these gaps, this dissertation examined youth psychopathology trajectories and transitions and whether psychopathology trajectories impact the association between family factors and MHSU. Specifically, the objectives were to: 1) identify distinct trajectories of psychopathology among youth with a CPI; 2) validate these trajectories by comparing with categorical classifications produced by a diagnostic interview tool; 3) examine predictors of the trajectory groups; 4) identify distinct subgroups of youth psychopathology; 5) examine transitions across these subgroups of psychopathology; 6) identify predictors of such transitions; 7) explore if youth psychopathology trajectories mediate the association between family factors and MHSU.
The first study developed a trajectory model using latent class growth analysis (LCGA) to examine the optimal number of trajectories of youth psychopathology and predictors of the different trajectories. Results indicated a three-trajectory model characterized as low-stable, moderate-decreasing, and high-decreasing trajectories. Older age, higher disability, greater parent psychological distress, and higher household income were associated with less favourable trajectories. Results demonstrate that youth with CPI exhibit different courses of psychopathology, and that different individual and family characteristics are associated with trajectory group membership.
The second study used latent profile analysis to identify four profiles of youth psychopathology: low psychopathology, primarily internalizing, primarily externalizing, and high psychopathology. Additionally, latent transition analysis was used to track transitions between these profiles over time. Many youths in the primarily internalizing subgroup transitioned to the low psychopathology subgroup over time. Further, youth classified in the high psychopathology subgroup from six to 24 months were more likely to have persistent psychopathology. These findings suggest that youth with CPI exhibit distinct profiles of psychopathology, with unique symptom combinations and patterns of change over time, emphasizing the potential for different mental health needs and trajectories within this population. Youth with CPI do not all experience psychopathology in the same way, and they may shift between profiles, suggesting dynamic changes in symptom patterns.
The third study conducted a path analysis to determine if youth psychopathology trajectories mediate the association between family factors (parent psychological distress and family functioning) and MHSU (i.e., contact with a healthcare professional). Results demonstrated that youth psychopathology trajectories (subclinical vs. low) mediate the association between parent psychological distress and contact with a healthcare professional. These findings support using a family-centred care approach to youth healthcare to minimize the burden on families and promote well-being and positive health outcomes.
This dissertation fills a critical gap in terms of knowledge of psychopathology and MHSU among youth with CPI. Taken together, these findings can be distilled into four themes: (1) call for integrated physical and mental healthcare; (2) early identification of psychopathology among youth with CPI; (3) adaptive treatment approaches to care; and (4) the need for family-centred care in youth mental health settings. Future longitudinal research should investigate transitions across psychopathology profiles over longer periods and investigate other potential mediators that facilitate or impede the use of mental health services for youth with CPI
Influence of Absorbency and Additives on Performance of Battery-Free IoT Water Leak Sensors
No full textLeak detection is a reliable solution for controlling the potentially destructive outflow and wastage of water. Several types of devices are used in domestic and industrial spaces; however, most have their power sources run out, and thus require battery change. The associated costs add to overhead expenditure of the user. This necessitates the use of leak detectors that are self-powered, having no use for external sources of power.
Integrating water leak detection systems with Internet of Things (IoT) technology such as Bluetooth low energy (BLE) and long-range (LoRa) protocols provides advantages such as real-time monitoring, which informs incidents and ultimately saves huge cost. The use of IoT-enabled sensors and cloud-based data analytics offers pre-emptive control mechanisms for prompt identification and containment of localized leaks. This helps reduce wastage of water and damage to property, both of which reduce costs as remote access through IoT networks guarantee instant notifications for preventative measures. Scalability fosters effortless deployment in residential, commercial, and industrial environments.
In a self-powered IoT water leak device, parameters such as capillary action and electrochemical reactions directly impact power generation and beacon activation. Energy generation and harvesting happen as water interacts with active materials within the sensor device. There must be a cathode and an anode, to interact with the leaking water which would be the electrolyte. Therefore, the materials selected to play such roles in the device are crucial for the desirable chemical interactions, once in contact with the leaking water.
In a water leak detector where the most crucial feature is sensitivity to water, capillary action is one of the most significant parameters to consider. Both the design of the sensor casing and channels through which the water travels, are to foster a seamless flow. Also, within the sensor chamber, each material in the stack must demonstrate capillarity. Therefore, porosity is key, as their pore sizes determine what material passes through and what might otherwise be trapped to impede the flow of the water being transmitted. Therefore, capillary action is explored for absorbent materials and the sensor casing. Both filter paper (FP) and fabric materials are examined, to ascertain which one gives optimally combined advantages for absorbency and repeatability. FP showed superior performance, due to its pore size. This advantage becomes particularly useful where additives are considered for the powder mixture.
Without additives, the stacked materials have only water to interact with. While this is sufficient to power BLE, it is not enough for LoRa technologies which require higher power. To account for this, additives can be included in the materials within the sensor stack. Salts are among such additives that can provide active ions when interacting with water. Subsequently, these ions facilitate electricity generation due to increased current. Therefore, the power output of the device can be increased when additives are introduced.
In previous similar works, it was shown that pure materials without any additives produce an open-circuit voltage (OCV) of 2 V and short-circuit current (SCC) of only 10 mA. This combination was able to power the sensor for beacon activation through 7 cycles of wetting-drying rounds of repeatability, but only for the BLE protocol. To solve for this limitation, NaCl was added in varied proportions. 10 wt.% NaCl was found to outperform other samples. After several rounds of repeatability, the values of current and voltage were observed to diminish. A sensor without NaCl typically lasts 7 rounds of repeatability, sensors containing NaCl last only about 3 rounds. The primary concern with the use of such additives may be an imminent trade-off between the increased power generation and possible corrosion which compromises shelf life.
One of the downsides of using additives to enhance power generation is the corrosion of metallic materials in the sensor. To study the effect of NaCl on the corrosion of the metallic material, and thus the shelf life of the sensor, electrochemical corrosion tests were performed. As expected, it was observed that higher salt content resulted in higher corrosion rate. Therefore, repeatability was significantly reduced in higher salt contents, thereby limiting the overall shelf life of the sensor. Ultimately, the use of salts should be limited and be specific to the target use case
Mathematical Models of Kidney Function: Effects of Hypertension and Circadian Rhythm
No full textHypertension induced by chronic angiotensin II (Ang II) infusion serves as a valuable experimental model for studying blood pressure regulation and the kidney's role in electrolyte and fluid homeostasis. The kidney's function is modulated by the renin-angiotensin-aldosterone system (RAAS) and circadian rhythms, with notable differences observed between males and females in the former. Under normotensive conditions, female rat nephrons exhibit lower Na+/H+ exchanger 3 (NHE3) activity in the proximal tubule but higher Na+ transporter activities along distal segments compared to males. Chronic Ang II infusion reduces NHE3 activity, shifts Na+ transport downstream, and promotes vasoconstriction, anti-natriuresis, and hypertension. These effects are further influenced by diurnal oscillations in glomerular filtration, electrolyte transport, and renal transporter regulation by circadian clock genes.
Using computational models of kidney function, this thesis explores two key areas: (i) the impact of Ang II infusion on segmental electrolyte transport and diuretic responses in male and female rat nephrons, and (ii) the influence of diurnal rhythms on the natriuretic and diuretic effects of loop, thiazide, and K+-sparing diuretics under normotensive and hypertensive conditions in male rats. Simulations suggest that NHE3 downregulation in the proximal tubule is a primary driver of natriuresis and diuresis, with stronger effects in males. In hypertension, the downstream shift in Na+ transport load amplifies the effects of diuretics, with hypertensive females exhibiting larger relative increases in Na+ excretion due to their higher distal transport load. Additionally, diuretic responses vary by time of day, with qualitatively similar diurnal oscillations observed in normotensive and hypertensive kidneys. These findings provide insights into sex-specific and time-dependent responses to hypertension and diuretic therapies, emphasizing the need to consider both physiological context and administration timing in treatment strategies
Evaluating the impact of participation in school-based physical education lessons on adolescent health and wellbeing in Ontario: Findings from the COMPASS study
No full textPhysical activity rates among adolescents in Canada are critically low; only about one in every three grade 9 students are meeting the recommended 60 minutes per day of moderate to vigorous physical activity (MVPA). These high rates of physical inactivity among youth are alarming, as physical activity is essential for both physical and mental wellbeing, and it sets the foundation for healthy habits in adulthood. School-based physical activities including physical education (PE) classes, and intramural and varsity sport programs are ideally situated for the promotion of physical activity as they can reach a large number of youth and overcome many of the barriers associated with extracurricular activities.
PE is designed to provide opportunity for youth of all ages to engage in physical activity that is structured into their weekly routine. However, in secondary school, a period that is critical for establishing healthy behaviour patterns for later in life, PE becomes non-mandatory for students in many provinces and territories across Canada, resulting in a missed opportunity to engage adolescents in regular physical activity. Ontario currently has the most lenient PE policy in Canada, with students only required to complete one secondary school-level PE course. To date, only four studies have examined the impact of PE programming in Canada on physical activity levels, only one of which included students from the province of Ontario. No published studies to date have explored the impact of PE participation on mental health outcomes among adolescents in Canada. The lack of evidence in this domain renders it challenging to determine the effectiveness of PE or make recommendations to enhance PE programs to maximize their impact on student health and wellbeing.
This dissertation aimed to provide a deeper understanding of the patterns of physical activity behaviours and the impacts of participating in non-mandatory secondary school PE on physical activity and mental health outcomes among adolescents in Ontario. Specifically, Study 1 characterized longitudinal physical activity profiles of non-mandatory PE participation, adherence to physical activity guidelines, and sport participation throughout secondary school. Study 2 quantified the impact of participation in PE on physical activity levels, over time. Study 3 quantified the impact of PE participation on student mental health, over time. This dissertation utilized linked longitudinal data from students in Ontario who participated in four consecutive years of the COMPASS Study (Time 1: 2015-16; Time 2: 2016-17; Time 3: 2017-18; Time 4: 2018-19). The COMPASS Study is a school-based prospective cohort study (2012-2027) that collects demographic, behavioural, and mental health data from students annually across Canada. Study 1 utilized a repeated measures latent class analysis to identify longitudinal physical activity profiles of adolescents in Ontario. Studies 2 and 3 utilized linear mixed models to estimate the average effect of PE participation on (a) minutes of MVPA (Study 2) and (b) symptoms of anxiety, (c) symptoms of depression, and (d) psychological wellbeing (Study 3), over time. Models in Studies 2 and 3 were adjusted using doubly robust propensity score methodology to account for self-selection biases that may influence PE participation.
Findings from Study 1 illustrated that there are distinct, clustered physical activity profiles among adolescents which vary by sex; three physical activity profiles were identified among both female and male students: Guidelines, PE & Sports, and Guidelines & Sports. A fourth profile was identified among male students only: Inactive. Study 2 demonstrated that participation in secondary school PE had a significant positive impact on MVPA levels over time, and effects were most pronounced for male students and during the semester of PE participation. Study 2 also illustrated that the benefits of PE remained present in the semester opposite to PE participation, suggesting that the benefits of PE extended beyond the MVPA accumulated during class-time. In Study 3, PE participation was not associated with symptoms of anxiety or depression, over time. Study 3 also found that male students enrolled (but not currently participating) in PE were found to have higher psychological wellbeing compared to those not enrolled in PE within the academic year.
This dissertation fills an important gap with respect to our understanding of PE programming in Ontario secondary schools. Findings from this dissertation revealed that many students are choosing not to enroll in PE, with a particularly high-risk subgroup of male adolescents showing low participation across several physical activity behaviours during secondary school. Among male students who elect to participate, PE was found to positively impact time spent in MVPA and psychological wellbeing. These results highlight the potential of PE for improving the health and wellbeing of adolescents, although low participation rates limit these benefits being experienced at the population-level. Importantly, all three studies identified sex-based differences in the physical activity profiles and the impact of PE on health outcomes; female students were found to have lower PE participation rates and experienced reduced benefits compared to male students. These result underscore the importance of promoting inclusive environments in PE to ensure health benefits are experienced by all adolescents, regardless of sex and other key characteristics. Findings from this dissertation offer valuable insights for public health programming, particularly within the school context; decision-makers in Canada should explore ways to increase PE participation across secondary schools, paying particular attention to female students and those not participating in other forms of physical activity
A Theoretical and Empirical Investigation into Payments for Watershed Ecosystem Services
No full textThis thesis contains three research chapters on the economics of Payments for Watershed Services (PWS). While each chapter covers a different aspect of PWS schemes, all three provide insights into the management of uncertainty in ecosystem services. The first chapter serves as an introduction to the problem and the main research question addressed by each paper.
Forested watersheds provide a variety of ecosystem services. Their economic valuation has increased significantly over the past decades, but the literature is fragmented and heterogenous and little has been done to systematically analyse estimated values. This study presents a global analysis of the economic valuation of forested watershed services. We address two methodological issues in the literature: the impact of ecosystem service classification on value estimates and sensitivity to scale. The latter is measured as the forested watershed area size compared to common practices to measure overall area size including other land cover and use. In the former case, we compare the detailed Common International Classification of Ecosystem Services with more simple and informal classifications in the literature based for example on the Millenium Ecosystem Assessment. We show that both the explanatory and predictive power of the estimated meta-regression models increase as we include more details about the valued ecosystem services and use more accurate estimations of the forested area size. Findings are, where possible, cross-validated with the existing forest hydrology literature. The study highlights the economic significance of maintaining forest cover in watershed areas and emphasises the role economics can play in determining high-value uses for ecosystem services.
The second chapter utilises the 2015 Survey of Drinking Water Plants, employing spatial regression models and mediation analysis to examine the relationships between land use, raw water quality, and treatment costs. The study reveals a significant and economically substantial impact of forest cover on reducing treatment costs, primarily through its influence on turbidity levels. Forest cover significantly reduces turbidity levels, thereby decreasing treatment costs. The results indicate that converting agricultural land to forest within a 5km buffer zone around a treatment facility can generate savings of 21.37 CAD per hectare per year. These savings diminish as the distance from the facility increases, with lower per hectare savings observed at the 10km buffer and sub-sub drainage basin scales. Further, the study accounts for spatial auto-correlation and the effects of wildfires on treatment costs. Wildfires are shown to lead to substantial increases in turbidity, significantly impacting treatment costs. The spatial error and lag models used highlight the importance of considering spatial dependencies in ecosystem service valuations. This research underscores the economic value of forest conservation and management in supporting water treatment processes and provides valuable insights for policymakers and stakeholders in water resources management. The study represents a significant step forward in understanding the interplay between land use, water quality, and treatment costs in Canada.
The main objective of the final study presented here is to develop a novel modelling framework in the context of Payments for Ecosystem Services (PES) to mimic and simulate behaviour of agents (e.g., landowners) providing ecosystem services and a principal (e.g., government, municipality) buying them under uncertainty. Uncertainty is defined as the case where the principal and agents lack precise knowledge on the parameters that govern ES output, but knows the range these parameters belong to. We compare contracts under two different decision-making paradigms, namely standard and robust optimisation. With robust optimisation, an uncertainty-averse principal designs a contract to maximise their worst-case outcome and obtain a performance guarantee, i.e., a minimum acceptable performance. The results show that with standard, input-based contracts, the only way for a principal to achieve this guarantee is to invest conservatively in ES. In this setting, the result holds with adverse selection and moral hazard. However, when input is unobservable, using output-based payments, the principal can achieve this performance guarantee only by sharing some of the value of ES output with the landowner
Investigation To A Neural Network Approach To Optimal Dynamic Allocation Problem In Defined Contribution Pension Plans
No full textIn this thesis, we propose a data-driven neural network (NN) optimization framework for solving a dynamic stochastic control problem under stochastic constraints. The objective function of the optimal control problem is based on expected wealth withdrawn (EW) and expected shortfall (ES) that directly targets left-tail risk. The optimal solution obtained from NN framework achieves high computational accuracy comparable to the Hamilton-Jacobi-Bellman (HJB) Partial Differential Equation (PDE) method. Additionally, the NN framework exhibits strong computational robustness, maintaining stable performance across different data distributions. Unlike traditional HJB PDE approaches, the NN framework can be extendable to high-dimensional multi-asset problems, overcoming the curse of dimensionality. To further enhance data diversity and improve generalization, we introduce TimeGAN and incorporate TimeGAN-generated data to generate historical financial time-series data, ensuring the robustness of model training
Learning-Based Safety-Critical Control Under Uncertainty with Applications to Mobile Robots
No full textControl theory is one of the key ingredients of the remarkable rise in robotics. Due to technological advancements, the use of automated robots, which was once primarily limited to industrial and manufacturing settings, has now expanded to impact many different parts of everyday life. Various control strategies have been developed to satisfy a wide range of performance criteria arising from recent applications. These strategies have different characteristics depending on the problem they solve. But, they all have to guarantee stability before satisfying any performance-driven criteria. However, as robotic technologies become increasingly integrated into everyday life, they introduce safety concerns. For autonomous systems to be trusted by the public, they must guarantee safety. In recent years, the concept of set invariance has been incorporated into modern control strategies to enable systematic safety guarantees. In this thesis, we aim to develop safety-critical control methods that can guarantee safety while satisfying performance-driven requirements. In the proposed strategies, we considered formal safety guarantees, robustness to uncertainty, and computational efficiency to be the highest design priorities. Each of them introduces new challenges which are addressed with theoretical contributions. We selected motion control in mobile robots as a use case for proposed controllers which is an active area of research integrating safety, stability, and performance in various scenarios. In particular, we focused on multi-body mobile robots, an area with limited research on safe operation. We provide a comprehensive survey of the recent methods that formalize safety for the dynamical systems via set invariance. A discussion on the strengths and limitations of each method demonstrates the capabilities of control barrier functions (CBFs) as a systematic tool for safety assurance in motion control. A safety filter module is also introduced as a tool to enforce safety. CBF constraints can be enforced as hard constraints in quadratic programming (QP) optimization, which rectifies the nominal control law based on the set of safe inputs.
We propose a multiple CBF scheme that enforces several safety constraints with high relative degrees. Using the multi-input multi-output (MIMO) feedback linearization technique, we derive conditions that ensure all control inputs contribute effectively to safety. This control structure is essential for challenging robotic applications requiring multiple safety criteria to be met simultaneously. To demonstrate the capabilities of our approach, we address reactive obstacle avoidance for a class of multi-body mobile robots, specifically tractor-trailer systems. The lack of fast response due to poor maneuverability makes reactive obstacle avoidance difficult for these systems. We develop a control structure based on a multiple CBFs scheme for a multi-steering tractor-trailer system to ensure a collision-free maneuver for both the tractor and trailer in the presence of several obstacles. Model predictive control serves as the nominal tracking controller, and we validate the proposed strategy in several challenging scenarios.
Although the CBF method has demonstrated a great potential for ensuring safety, it is a model-based method and its effectiveness is closely tied to an accurate system model. In practice, model uncertainty compromises safety guarantees and may lead to conservative safety constraints, or conversely, allow the system to operate in unsafe regions. To address this, we explore developing safety-critical controllers that account for model uncertainty. Achieving this requires combining the theoretical guarantees of model-based methods with the adaptability of data-driven techniques. For this study, we selected Gaussian processes (GPs) which bring together required capabilities. It provides bounds on the posterior distribution, enabling theoretical analysis, and producing reliable approximations even with a low amount of training data, which is common in data-driven control. The proposed strategy mitigates the adverse effects of uncertainty on high-order CBFs (HOCBFs). A particular structure of the covariance function is designed that enables us to convert the chance constraints of HOCBFs into a second-order cone constraint, which results in a convex constrained optimization as a safety filter. A discussion on the feasibility of the resulting optimization is presented which provides the necessary and sufficient conditions for feasibility. In addition, we consider an alternative approach that uses matrix variate GP (MVGP) to approximate unknown system dynamics. A comparative analysis is presented which highlights the differences and similarities of both methods. The proposed strategy is validated on adaptive cruise control and active suspension systems, common applications in mobile robots.
This study next explores the safety of switching systems, focusing on cases where system stability is assured through control Lyapunov functions (CLFs) and CBFs are applied for safety. We show that the effect of uncertainty on the safety and stability constraint forms piecewise residuals for each switching surface. We introduce a batch multi-output Gaussian process (MOGP) framework to approximate these piecewise residuals, thereby mitigating the adverse effects of uncertainty. We show that by leveraging a specific covariance function, the chance constrained safety filter can be converted to a convex optimization, that is solvable in real-time. We analyze the feasibility of the resulting optimization and provide the necessary and sufficient conditions for feasibility. The effectiveness of the proposed strategy is validated through a simulation of a switching adaptive cruise control system