Chemo-rheological Characterization of Asphalt Binders Using Different Aging Processes

The performance and longevity of asphalt pavements depend heavily on the properties of asphalt binders, which are affected by aging, binder modifications, and the incorporation of reclaimed asphalt pavement (RAP) materials. However, significant gaps exist in understanding the long-term chemical and rheological changes induced by aging processes (particularly with respect to differences between thermo-oxidative aging and UV exposure), and in the use/standardization of chemical analytical techniques such as Fourier Transform Infrared (FTIR) and Nuclear Magnetic Resonance (NMR) spectroscopy for binder characterization. Furthermore, the behaviour in RAP-virgin binder blends, along with the influence of bio-based rejuvenators and anti-aging additives under different aging conditions, remains underexplored. Addressing these gaps are crucial to developing more durable, sustainable pavements. This thesis bridges these research gaps through comprehensive investigation of chemo-rheological binder characterization, combining experimental testing with advanced analytical tools and varying aging methods. The findings offer essential insights into binder aging, rejuvenation strategies, and modification techniques, with significant implications for pavement durability and environmental sustainability. The first chapter presents an evaluation of Attenuated Total Reflection-Fourier Transform Infrared (ATR-FTIR) spectroscopy combined with functional group and multivariate analysis techniques to characterize asphalt binders. The research identifies challenges in repeatability across binder sources and aging states demonstrating the importance of standardized protocols for improving reliability. Repeatability as described by AASHTO standards is listed in the precision and bias statement as single operator precision. This is the allowable difference in two test results measured under the repeatability conditions (same asphalt binder, measured by the same operator, on the same piece of equipment in the same lab). Principal Component Analysis (PCA) and k-means clustering successfully classified binder types and aging states, with large quantity (LQ) sample preparation yielding more consistent results than small quantity (SQ) preparation. These findings underscore the need for uniform procedures in binder analysis, addressing inconsistencies prevalent in the current literature. The second part of the thesis investigates the impact of Styrene-Butadiene-Styrene (SBS) polymer modification on binder performance and oxidative resistance. Using Nuclear Magnetic Resonance (NMR) and ATR-FTIR spectroscopy, along with PCA and Partial Least Squares Regression (PLSR), the research highlights the ability of SBS to enhance high-temperature performance and slow thermo-oxidative aging. This work not only confirms previous findings on SBS but also provides new insights into the molecular interactions contributing to aging resistance. The study fills a gap in understanding how SBS-modified binders behave under various aging scenarios, offering a deeper perspective on polymer-modified asphalt technologies. The thesis also addresses a critical gap related to UV-induced aging, which has been underexplored in comparison to thermo-oxidative aging. A novel UV aging chamber was developed to simulate real-world environmental conditions, incorporating UV exposure, water spray cycles, and controlled heating at 70°C. Comparative analysis revealed that different additives exhibit varying effectiveness under UV and thermo-oxidative conditions. Zinc diethyldithiocarbamate (ZDC) showed strong resistance to thermo-oxidative aging but limited efficacy under UV aging, while ascorbic acid (Vit. C) accelerated aging under UV exposure, contrary to expectations. These findings emphasize the challenges involved in designing effective anti-aging strategies for asphalt binders, demonstrating the value of combining conventional rheological tests with spectroscopic techniques and further highlighting the need for more targeted approaches to additive selection and development. This thesis advances the understanding of asphalt binder behaviour and aging processes by integrating chemical, rheological, and multivariate analysis techniques. It offers critical contributions to the standardization of binder characterization protocols, the optimization of polymer-modified asphalt technologies, and the development of more effective anti-aging strategies. The research also demonstrates the potential of machine learning and artificial intelligence (AI) in predicting binder performance from spectroscopic data using multivariate analysis, paving the way for future innovations in asphalt binder characterization. In conclusion, the work in this thesis addresses significant gaps in the literature, providing new insights into aging mechanisms, additive/rejuvenation strategies, and RAP binder interactions. By combining chemical analysis, rheological testing, and multivariate techniques, this research contributes both to academic knowledge and practical pavement engineering, promoting the development of more sustainable, long-lasting asphalt pavements

Assessment of the Proposed Policies for a Carbon Capture and Storage Regulatory Framework in Ontario

Since 2022, Ontario has been investigating the possibility of developing a Carbon Capture and Storage (CCS) framework as they aim to reduce carbon emissions and align with the federal government’s goals of net-zero emissions by 2050. This CCS regulatory framework should focus on hard-to-abate sectors where alternative renewable energy technologies are in their early stages, or they are difficult to be transitioned. However, within the research field of CCS in Ontario from a policy perspective, there are minimal journal articles and grey-literature documents that discuss this topic. Therefore, the purpose of this thesis is to understand and analyze Ontario’s proposal of their regulatory framework for CCS and to give recommendations to the CCS framework by comparing it against the information gathered from other jurisdictions (Alberta, Saskatchewan, the United States, Europe and Australia). Key research questions are 1. How can the knowledge gained from other regions regarding CCS help Ontario's hard-to-abate sectors to understand approvals, licensing, and liability? 2. What are some other necessary policies that Ontario would need to expand upon and potentially adopt from various jurisdictions? And 3. How did companies and governments in other jurisdictions communicate to the public about the need for this technology? The thesis first developed a literature review to compare and contrast policies from other jurisdictions by researching and synthesizing various peer-reviewed journal articles and grey literature. Then, a semi-structured interview was needed to explore any unique perspectives from interviewees with expertise in CCS, and also to understand whether the results aligned with the information from the literature review. Following the interviews, the analysis of the results were accomplished by using ‘codes’ and ‘themes’, which allows for a simplified understanding of which information is unique. As a result, there were unique findings from the interviews such as ensuring proper industries are utilizing CCS, explaining the purpose of CCS, ensuring that the regulatory framework for CCS is properly developed, and the potential for CCS to utilize a carbon market through an Emissions Trading System (ETS). In November 2024, Ontario introduced Bill 228, which contains an Act called the Geologic Carbon Storage Act, 2024. This Act contains the key core components of the regulatory framework, such as ownership, liabilities, and approvals and assessments. As a result, a description and analysis of this Act was undertaken to understand how it compares against my research findings. In conclusion, to answer the first research question, the findings resulted in requiring Ontario to vest in the pore space, implement a unitization statue, implement a transfer of liabilities once certain pre-conditions are met and a post-stewardship fund to cover liability costs. As for the second research question, the other necessary policies include expanding upon environmental assessments methods, using a systems analysis approach to understand the outcomes of developing CCS, incorporating CCS into carbon pricing schemes, and Ontario’s plans on how they should utilize their CCS. The findings for the final research question recommend that the Ontario government and companies recognize the social demographic backgrounds of Ontario; ensure that Ontario is integrating and engaging with communities closely; explaining the downsides of not developing a CCS project; and respecting a community’s decision if they do not wish to engage with the project. Bill 228 is consistent with these findings, namely the inclusion of a liability transfer; a stewardship fund to cover the liabilities for the Crown; unitization of pore spaces; risk management; monitoring, measurement and verification (MMV); emergency response; and various approvals and assessments. However, the ownership of pore spaces deviates from these findings, as Ontario vests pore ownership to the surface owners but still allows the Crown to vest in the pore space when required

Delay Averse Crew Pairing Optimization

Airline operations involve many complex and interdependent decisions. These include flight scheduling, fleet assignment, maintenance routing, and crew scheduling. Crew scheduling is particularly important, as crew costs are the second-largest expense in airline operations. In practice, delays are common and can cause major disruptions to crew schedules. Most crew pairing models are deterministic and aim to minimize planned costs. However, they often ignore operational uncertainties, such as delays. As a result, they may perform poorly in real operations. This is critical because crew payments are based on actual flight times. This thesis addresses the delay averse crew pairing problem. We propose an optimization model with a delay cost component to create more reliable pairings. The model is based on a duty network structure, and utilizes the pay-and-credit scheme to account for crew payments. We solve the model using Lagrangian relaxation and perform extensive testing on real data. We compare the delay averse model to the nominal case and conduct sensitivity analysis to demonstrate how changes in cost parameters and delay levels affect the solutions. Finally, we evaluate the solutions using simulation based on historical flight delay data. The results show that the delay averse model creates more reliable pairings. These pairings are better at absorbing delays and reducing total propagated delays

Evaluating remote sensing and modeling approaches for estimating net ecosystem exchange in Canadian peatlands

Peatlands represent a type of wetland, that has accumulated a layer of organic material or peat, resulting in high organic carbon (C) accumulation in this ecosystem. Peatlands hold up to one-third of the global organic C stock, and specifically peatlands located at high latitudes in the Northern Hemisphere, or northern peatlands, store a large proportion of the of the total peatland organic C stock. However, this organic C may be in jeopardy, as warmer temperatures may lead to increases in both the C uptake and output. There is some uncertainty as to how northern peatlands C sink function will be impacted by climate warming, and conventional models of peatland C cycling have been constrained to site-specific applications rather than national-scale analyses. Moreover, in-situ measurements are limited in northern peatlands, due to the remoteness of these sites, but also due to equipment limitations under low temperature and light conditions. In this thesis, I specifically looked at one component of C flux, the net ecosystem exchange (NEE) of CO2. To enable forecasting of NEE of CO2 fluxes in peatlands under future scenarios, or to generate real-time estimates where no in-situ measurements exist, machine learning algorithms trained on in-situ CO2 fluxes from the eddy covariance (EC) technique must be applied. Remotely sensed or gridded climate data products represent potentially important inputs to these modeling applications, as they are widespread both geographically and temporally enabling flux estimation for broader geographic domains. In Chapter 2, I explored the possibility of using the remotely sensed and modeled Soil Moisture Active Passive Level 4 Global Daily EASE-Grid Carbon NEE (SMAP-NEE) data product to determine NEE in Canadian peatlands. I acquired nine years (2015–2023) of SMAP-NEE data for five peatlands. I also acquired a subset of year-round eddy covariance NEE (EC-NEE) measurements within this time frame at each of the five peatland sites. The analyses showed that the SMAP-NEE data product reports a stronger growing season (GS) sink and a weaker non-growing season (NGS) source than the EC-NEE measurements. As a result of this finding, I used the relationship between SMAP-NEE and EC-NEE to produce a Corrected-SMAP-NEE dataset, which provides an estimate of seasonal and annual CO2 budgets. The data analyses of the Corrected-SMAP-NEE dataset showed that NGS CO2 emissions represent a variable proportion (33%–256%) of the GS CO2 uptake, and when these NGS emissions were accounted for, the annual CO2 sink strength was reduced proportionally. Furthermore, this study showed that longer growing seasons were consistent with greater annual net CO2 uptake at these five peatland sites from 2015-2023. The findings highlight the importance of considering the NGS when evaluating annual northern peatland C budgets. This chapter also provides evidence that existing algorithms leveraging remotely sensed and gridded climate data products to model NEE need improvement for peatlands. In Chapter 3, I compiled year-round measurements of EC-NEE from 15 Canadian peatland sites and coupled these target data with 34 hydroclimatic predictor variables (features) from remote sensing and gridded climate data products. The models were trained, validated, and tested using four algorithms: ElasticNet Regression (EN), Light Gradient-Boosting Machine (LGBM), Random Forest Regression (RFR), and Support Vector Regression (SVR). A comprehensive feature importance and selection workflow including hierarchical clustering, Gini importance, and minimum redundancy maximum relevance (mRMR) analysis was followed. Model performance stabilized at eight features, which were (relative importance shown in parentheses): evapotranspiration (40%), shortwave radiation (19%), burn area index (11%), normalized difference snow index (10%), snow water equivalent (7%), climate water deficit (4%), wind speed (4%), and soil moisture (4%). I found the best performing model to be the RFR model with these eight features (R2 = 0.76; RMSE = 0.31 g C m−2 day−1). I also assessed the generalizability and transferability of the top-performing model via a leave-one-ecoregion-out sensitivity analysis as well as on six external validation sites. The RFR model had the highest generalizability within the Taiga Plains ecoregion and the lowest generalizability within the Boreal Plain ecoregion. When testing the model on the external validation sites, performance metrics were comparable to the internal testing data for sites outside of the ecoregions represented in the training data. The findings demonstrate that the eight-feature models can be confidently upscaled to national extents, offering a clear pathway to improve Canada’s spatially explicit CO2 emission inventories

NP-hardness of testing equivalence to sparse polynomials and to constant-support polynomials

Given a list of monomials of a n-variate polynomial f over a field F, and an integer s, decide whether there exists an invertible transform A and a b such that f(Ax + b) has less than s monomials. This problem is called the Equivalence testing to sparse polynomials (ETsparse). It was studied in [GrigorievK93] over Q, in this work, they give an exponential in n^4 time algorithm for the problem. The lack of progress in the complexity of the problem over last three decades raises a question, is ETsparse hard? In this thesis we give an affirmative answer to the question by showing that it is NP-hard over any field. Sparse orbit complexity of a polynomial f is the smallest integer s_0 such that there exists an invertible transform A such that f(Ax) has s_0 monomials. Since ETsparse is NP-hard hence computing the sparse orbit complexity is also NP-hard. We also show that approximating the sparse orbit complexity upto a factor of s_f^{1/3-\epsilon} for any \epsilon \in (0,1/3) is NP-hard, where s_f is the number of monomials in f. Interestingly, this approximation result has been shown without invoking the celebrated PCP theorem. [ChillaraGS23] study a variant of the problem which focus on shift equivalence. More precisely, given f over some ring R (the input has the same representation as in ETsparse) and an integer s, does there exists a b such that f(x + b) has less than s monomials. It is called the SETsparse problem, [ChillaraGS23] showed that SETsparse is NP-hard when R is an integral domain which is not a field; we extend their result to the case when R is a field. Finally, we also study the problem of testing equivalence to constant-support polynomials; more precisely, given a polynomial f as before and with support \sigma, does there exists an invertible transform A such that f(Ax) has support \sigma -1. We call this problem ETsupport. We show that ETsupport is NP-hard for \sigma >= 5 and over any field

Understanding Human Decision Variability and the Effects of AI System Data Modality on Trust and Acceptance in Human–AI Collaboration : A Human-Centred Approach to Designing AI Systems in Healthcare Contexts

Effective decision-making is a complex cognitive process that plays a crucial role in high-stakes domains such as healthcare, where inconsistencies in judgment can significantly impact outcomes. It varies widely between individuals, particularly across levels of expertise [Rasmussen, 1983], often leading to variability and inefficiencies [Curran et al., 2022], a phenomenon well-documented in cognitive and decision sciences. As Artificial Intelligence (AI) becomes increasingly integrated into various domains, including finance, transportation, and healthcare, it presents new opportunities to enhance human decision-making. With recent advancements, AI has the potential to mitigate these decision-making challenges, such as inter-user variation, by providing standardized, data-driven recommendations. It can support novice reasoning by guiding decision processes and complement expert intuition when aligned with users’ strengths and limitations [Inkpen et al., 2023]. However, other research has shown that standalone AI systems cannot be fully relied upon due to inherent biases, limited contextual understanding, and an inability to adapt dynamically to the complexities of human decision-making. These limitations necessitate a shift toward human-AI collaborative systems, where AI serves as a complementary tool to enhance human performance. Effective collaboration, however, depends on calibrated trust and high acceptance of AI systems, influenced by numerous factors, including the underexplored effect of AI system data modality on user trust and acceptance. This thesis investigates the variations in decision-making strategies between novices and experts and examines how AI systems can bridge these gaps through human-centric design. It further explores how data modality in AI systems, particularly unimodal versus multimodal data, affects human-AI collaboration. A two-phase study was conducted in the healthcare domain, focusing on glaucoma diagnosis as a specific case study, and employed a mixed-method approach combining qualitative interviews and quantitative evaluations. The first phase examined variations in decision-making strategies between novices and experts. It was found that experts adopted more dynamic and efficient approaches by integrating a wider range of factors, emphasizing progression analysis, identifying complex patterns and correlations, and dynamically balancing positive and negative decision factors based on contextual severity. They demonstrated cognitive efficiency by filtering out extraneous information and prioritizing critical data points. In contrast, novices relied on more structured and analytical methods, often overemphasizing explicit indicators and struggling to balance conflicting evidence. Their decision-making factors also varied significantly across different scenarios. Furthermore, the impact of data availability was evident, with novices being more adversely affected by limited data compared to experts. The second phase evaluated user interactions with unimodal and multimodal AI systems designed for glaucoma diagnosis, measuring trust and acceptance using statistical methods. Multimodal systems consistently outperformed unimodal systems by integrating diverse data sources that mirrored how clinicians process information, leading to better alignment with their workflow. This, in turn, led to significantly higher trust and acceptance for multimodal systems, compared to unimodal systems with significant differences. (p < 0.01). Decision-making performance of optometrists also improved, with multimodal systems achieving higher performance than unimodal systems. An interaction effect between user factors (expertise, gender) and system type was also observed, with notable differences in accuracy and confidence levels. These findings show the need for human-centric AI systems that support novice learning and expert decision-making, leveraging data modalities that align with cognitive processes to foster calibrated trust and high acceptance. By enhancing human-AI collaboration, such systems can improve decision-making consistency and optimize outcomes across diverse contexts. This thesis makes interdisciplinary contributions to human factors, AI, and optometry by investigating cognitive variability in glaucoma diagnosis and examining how data modality influences trust, acceptance in human-AI collaborative environments. In the domain of human factors, it offers insights into how clinical expertise shapes diagnostic reasoning, revealing distinct approaches to data use, heuristic application, and uncertainty management between novices and experts. In the domain of AI, it proposes the design of human-centered AI systems that support optometrists with varying levels of expertise and also evaluates unimodal and multimodal decision-support systems, demonstrating that multimodal systems more closely align with clinicians’ workflows, resulting in higher trust, acceptance, and diagnostic performance. In optometry, the research examines how clinicians interpret glaucoma data, highlighting differences in reasoning strategies such as progression analysis and data prioritization. It informs the development of AI tools that align more closely with optometrists’ decision-making processes and integrate seamlessly into routine clinical workflows. In conclusion, this thesis explores variations in human decision-making and demonstrates the value of human-centric AI systems in supporting both novices and experts. It identifies data modality as a key factor influencing trust and acceptance, providing a rationale for using the same data clinicians rely on to achieve better workflow alignment, leading to higher trust and greater system acceptance. These insights offer a strong foundation for future AI integration in optometry and other high stakes medical domains

Optimization and Characterization of Laser Powder Bed Fusion of AlSi10Mg for Complex Aerospace Structures

Manufacturing for the aerospace industry is dominated by the production of components which operate in extreme environments under high loads and must be as light-weight as possible. Aerospace components are typically highly complex and produced in relatively small quantities, whereby manufacturing typically involves specialized tooling, with high costs due to complexity. Additive Manufacturing (AM) presents a potential solution to these challenges. AM presents unparalleled design freedom, since increasing the complexity of a part does not increase the cost. Not only can AM be used to aid in the manufacturing of complex aerospace components, but it can enable new designs that can significantly reduce weight and increase the performance of assemblies and systems. In particular, laser powder bed fusion (LPBF) is one of the most widely adopted metal AM technologies, as it provides high feature resolution, while achieving fully dense parts, with tailored microstructure. Although there are a number of use cases for LPBF in aerospace, there are still a number of manufacturing and design constraints that have limited its adoption. This work adds to the body of knowledge towards addressing these limitations by investigating several topics important to the practical use of LPBF for the manufacturing of aluminum aerospace components with complex design architectures, particularly in considering thin walled structures with complex surface inclinations: tailoring surface topography, studying mechanical properties, and customizing design architectures for structural and performance criteria. A case study is presented considering a key structural member for a high powered sounding rocket. The structural member is re-designed for AM to demonstrate a new methodology for the design of lightweight structures under compressive loading. Simulations were deployed which used the stress response of an initial solid beam design to create a functionally graded hollow shell that was then augmented with surface lattices to further prevent thin wall buckling. After the part was printed from AlSi10Mg using LPBF, it was subjected to compression testing. A novel test fixture was designed and validated to allow fixed-end free-end buckling testing. The design exhibited exceptionally even stress distribution and very high buckling resistance, demonstrating a 98% increase in strength compared to the conventional part design, despite the two designs having equal mass. One of the largest design constraints of LPBF is the inability to print structures with steep overhangs. When these overhangs can be printed, the downward facing surface, known as downskin, typically has a very high surface roughness. To enable the printing of structures with steeper overhangs and better surface roughness, a number of process parameter combinations are tested across downskin angles ranging from 75◦ to as low as 20◦. A modeling approach is proposed for predicting the effect of process parameters on downskin roughness and the predicted performance is found to align closely with the experimentally measured roughness. A set of process parameters are identified that enable the printing of parts at angles as low as 20◦ with good surface roughness. The tensile properties of thin-walled structures printed from AlSi10Mg are then investigated using a select range of process parameters. The effect of part thickness, part orientation, and part finishing (machining vs. as-built) on the measured tensile properties of the samples is discussed. The Young’s modulus, yield strength, ultimate tensile strength, and elongation at break are characterized for these process parameters. The effect of overhang angle on tensile properties is measured, with significant reductions in performance found at low downskin angles. The design of a number of complex aerospace components which rely on the results of this work are then discussed. This includes multiple parts slated to fly on the 2nd ever Canadian built liquid rocket

Environment, Not Social or Governance, Oh My: Sustainability Priorities in Canadian University Sustainability Documents

This author accepted manuscript is deposited under a Creative Commons Attribution Non-commercial 4.0 International (CC BY-NC) licence. This means that anyone may distribute, adapt, and build upon the work for non-commercial purposes, subject to full attribution. If you wish to use this manuscript for commercial purposes, please contact [email protected]/methodology/approach Comparing sustainability policies with the overarching goals of the United Nations Sustainable Development Goals through the approach of narrative policy analysis, this research explores the priorities present and what patterns emerge in these priorities. Purpose Universities are significant in their sustainability action not just for the education that they provide, but how they operate as mini cities, with massive environmental footprints, meaning that, their sustainability direction matters. As a result, this research investigates the priorities present in university sustainability policy documents from 33 Canadian universities. Findings Findings include that across Canada, planet related sustainability priorities, particularly those focused on cities, climate action and consumption are the most present, while people and prosperity elements of sustainability often fall outside the scope of these policies. Some regional variation emerges in key areas such as energy and climate action, and size also sees a correlation to prioritization of specific areas of sustainability such as waste. Originality/value This research will be of interest to researchers in the emerging field of sustainability in higher education, practitioners and administrators in university sustainability and policy makers looking to understand sustainability prioritization shape the future of nuanced sustainability directions.Social Science and Humanities Research Council of Canada's Canada Graduate Scholarship Doctoral Program, 767-2022-1443 || Balsillie School of International Affairs', Balsillie Fellowship || University of Waterloo's President's Graduate Scholarship

Multi-scale Modelling of Neurosteroid-mediated Seizure Trajectories in Childhood Absence Epilepsy

Childhood absence epilepsy (CAE) is a pediatric generalized epilepsy disorder characterized by brief episodes of impaired consciousness and distinctive 2.5--5 Hz spike-wave discharges (SWDs) on electroencephalography. With a well-established genetic aetiology, this condition tends to resolve spontaneously during adolescence in most cases. While several mechanisms have been proposed for remission, understanding remains insufficient to guide early intervention practices. In this thesis, we first utilize a conductance-based thalamocortical network model that exhibits characteristic SWDs, to demonstrate that allopregnanolone---a progesterone metabolite known to enhance GABAa receptor-mediated inhibition---has an ameliorating effect on SWDs. To investigate the divergence between this finding and clinical observations, we developed an enhanced thalamocortical model that incorporates a layered cortical structure to explore regional cortical heterogeneity and frontocortical connectivity as potential resistance factors to ALLO-mediated recovery. Our results suggest that non-resolving CAE may be due not only to increased frontocortical connectivity but also to the composition of cell types within the network. Specifically, a higher proportion of bursting-type cells may prevent the therapeutic effects of allopregnanolone. We extended our investigation to examine whether these findings apply to CAE caused by different genetic mechanisms, particularly mutations in sodium channel genes by modelling their effects at the individual neuron level. Furthermore, we examined the degree to which these alterations lead to network-level pathological activity, as well as the influence of ALLO on these genetically distinct networks. Our results demonstrate that ALLO facilitates recovery from SWDs regardless of the underlying mutation type. However, enhanced frontocortical connectivity prevents recovery in some mutation types, particularly when mutation effects are severe. Altogether, the multi-scale computational framework developed in this thesis demonstrates that CAE remission is determined by complex interactions between hormonal influences, genetic factors, and network connectivity patterns. The results suggest that certain genetic mutations may predispose individuals toward either remission or non-remission, which can be further modulated by connectivity profiles. In particular, enhanced frontocortical connectivity appears to be a significant factor in resistance to hormone-mediated remission. Additionally, this thesis develops techniques for analyzing transitions between distinct dynamical states in neural systems, incorporates genetic and hormonal factors into conductance-based models, and provides a computational framework to identify key parameters governing epileptic activity. These approaches not only advance our understanding of CAE specifically, but offer generalizable insights into the mathematical modelling of neurological conditions characterized by spontaneous shifts in brain dynamics

Applicability of Adaptive Co-Management within Indonesian Small-Scale Fisheries.

Small-Scale Fisheries (SSF) governance has historically excluded small-scale fishers from participating in decision-making processes, negatively influencing millions of livelihoods. Governance of SSF is complex due to interactions between users and the environment, both with varying influence on the system. Indonesia is of particular importance for SSF governance due to its archipelagic structure, fishing culture and the direct link between economic viability and SSF. Indonesian SSF provide livelihood, nutrition, and economic security to millions of fishers. Indonesian SSF however face, illegal and unreported fishing practices, fishing location disputes, pollution, poor living conditions, declining fish stocks and extreme volatile weather conditions. Effective governance strategies for SSF that can adapt to dynamic conditions within Indonesian SSF are critically needed. This thesis aims to explore the strength of adaptive co-management indictors present within Indonesian SSF, and how current governance of SSF can be transitioned, aiding in the transition of these fisheries from vulnerability to viability. Adaptive co-management is a governance approach that combines co-management and adaptive management, while integrating the practices of learn-by-doing, social memory and social networks into governance proceedings. This thesis indicates that adaptive co-management is an effective governance approach for complex social-ecological systems such as SSF. With adaptive co-management providing an avenue to facilitate vulnerable to viable SSF transitions. Long-term institutional support, effective capital building and social capital, were the strongest indicators of adaptive co-management, marking these critical for future development in SSF