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Development of Novel Macromolecular Organic Semiconductors for their Applications in Organic Electronics
(Université d'Ottawa / University of Ottawa, 2026-05-15) Cyr, Mélanie; Brusso, Jaclyn; Lessard, Benoît H.
Organic semiconducting (OSC) materials are now more than ever being exploited in modern technology for the development of next-generation organic electronic devices. These types of materials hold many advantages over their inorganic counterparts such as their mechanical flexibility, solution processibility, lighter density and strong light-matter interactions, to name a few. Nevertheless, their most attractive feature remains their ease of functionalization to adjust and optimize their optical and electronic properties. The slightest change in molecular structure can have a significant impact on the materials' ability to interact with analytes, absorb light and transport charges. This has led OSCs to be exploited in a wide array of fields including optoelectronics, sensors and actuators, energy storage, printed electronics, biomedical applications and more. This thesis looks to developing new donor-acceptor (D-A) small molecules and porphyrinoid macrocycles and evaluate their potential as n-type and p-type OSCs in organic thin-film transistors (OTFTs) as well as their potential in chemical sensing. I synthesized and characterized each novel material at the molecular level and in the solid state when processed into thin-films. The chemical derivatizations allowed to adjust the frontier molecular orbitals (FMOs) and molecular bandgap adopted by each material. I also studied their semiconducting potential in different OTFT device architectures as well as explored different device fabrication conditions to optimize their semiconducting abilities. In the latter work of this thesis, I also explored electropolymerization of silicon phthalocyanine materials to create sensing films onto quartz microbalance (QMB) sensors for mass-based detection and interdigitated electrode (IDE) sensors for electronic-based detection of various gases and volatile organic compounds (VOCs). Overall, this thesis demonstrates the design and functionalization of OSC materials and portrays the importance of structure-property relationships adopted by these materials as they are processed into thin-films for specific organic electronic applications.
Coastal Biogeochemical and Microbial Responses to Exports from a Small Rain-Dominated River Plume in the Northeast Pacific Coastal Temperate Rainforest
(Université d'Ottawa / University of Ottawa, 2026-05-15) Whalen, Rebecca; St. Pierre, Kyra
The Northeast Pacific Coastal Temperate Rainforest (NPCTR) spans over 2000 km from Alaska to California and includes 1000's of diverse small rain-dominated coastal watersheds that, despite covering 24% of the surface area, deliver 33% of discharge to the Pacific Ocean. Coastal ecosystems receiving these inputs are potential hotspots of biogeochemical transformation but remain understudied due to logistical and historical biases of the fields of oceanography and limnology. This lack of integration across these fields limits our understanding of how terrestrial exports from small catchments affect coastal carbon and nutrient dynamics, microbial community structure, and overall ecosystem ecology. This thesis addresses this problem by examining a river-ocean estuary on Quadra Island (British Columbia, Canada) to assess how rain-dominated watershed exports shape coastal microbiology and biogeochemistry. Water samples collected within a small river plume (0-27.9 PSU) at 0, 15, and 50 cm depth across seven sites from Hyacinthe Creek into Hyacinthe Bay were analyzed for phosphate, silica, carbon and nitrogen species and isotopes, dissolved organic matter (DOM) composition, and putative active (RNA) and present (DNA) microbial communities. The core of this thesis is a manuscript that analyses carbon and nutrient concentrations, stable isotope signatures, and microbial community diversity to investigate how vertical and horizontal salinity gradients affect dispersion of organic and inorganic matter, and its potential effects on microbial ecology. Surface samples and salinity mapping revealed distinct biogeochemical and microbial signatures relative to underlying waters, dissipating with increasing distance from the creek mouth. Nutrient and organic matter concentrations and signatures increased with distance from the creek mouth, deviating from conservative mixing and suggesting active reprocessing. Microbial communities exhibited distinct horizontal and vertical structures, supporting the existence of a river-to-sea microbial continuum, and revealing taxa that may be disproportionately more active compared to the total community in transitional waters. Salinity, organic carbon, condensed aromatic compounds, and isotopic signatures (δ¹³C-DOC, δ¹³C-POC, δ¹⁵N-PN) were highlighted as key drivers of coastal microbial community structure. Cumulatively, this study explores important biogeochemical trends through small-scale vertical and horizontal extents, a scale often overlooked in oceanography and larger river plume systems. This work further highlights that small, rain-dominated river plumes may strongly influence biogeochemical cycling and microbial dynamics in NPCTR estuaries, and that their impacts are concentrated in a vertically shallow but active surface layer. This estuary approach advances our current understanding of biogeochemical dynamics in small rain-dominated river plumes of the NPCTR, while suggesting the need for a finer-resolution lens to study small rain-dominated watersheds in future.
The Influence of Exercise Training on Cardiovascular Health Outcomes in Adults with Cardiac Implantable Electronic Devices
(Université d'Ottawa / University of Ottawa, 2026-05-14) Roque Marçal, Isabela; Reed, Jennifer L.; Adamo, Kristi B.
Aims and Methods: The overall purpose of this dissertation was to examine the influence of exercise training on cardiovascular health outcomes in patients with cardiac implantable electronic devices (CIEDs). Study 1 retrospectively examined the changes in physical (e.g., cardiorespiratory fitness [CRF]), and mental health (e.g., anxiety levels) outcomes in patients with CIEDs completing an exercise-based cardiovascular rehabilitation (CR) program. Study 2 retrospectively assessed the associations of CR on major adverse cardiovascular events (MACE) among adults with CIEDs. Study 3 investigated, through a systematic review and meta-analysis, sex differences in CRF changes following exercise training in women and men with CIEDs. Study 4 tested the feasibility of a pilot randomized controlled trial comparing a 12-week virtual program of high-intensity interval training (HIIT) and moderate-to-vigorous intensity continuous training (MICT) in women with CIEDs. Results: Study 1 showed that patients with CIEDs (n = 252, 26% females) completing CR improved CRF and reduced anxiety and depression levels in patients with CIEDs. Study 2 found that CR was not associated with lower 5-year risk of MACE comparing propensity score-matched CIED patients with and without CR (n = 344, 23% females), whereas females derived greater benefit from CR. Study 3 demonstrated no sex differences in CRF following exercise among patients with CIEDs (n = 365, 22% women). Study 4 revealed that, among women with CIEDs (n = 20), a 12-week virtual HIIT intervention was feasible, whereas virtual MICT was not. Conclusions: Findings of this dissertation highlight (i) the importance of CR to improve CRF, a strong predictor of mortality, in individuals with CIEDs, (ii) the need of large-scale studies to understand the impact of CR on MACE in patients with CIEDs, (iii) a call to action to advance sex-specific inclusion and reporting practices in exercise trials with CIEDs, and (iv) remote HIIT is a feasible and safe exercise alternative to women with CIEDs, where results on key cardiovascular data can inform future trials.
Learning Interpretable Theories for Complex Neural Systems
(Université d'Ottawa | University of Ottawa, 2026-05-14) René, Alexandre; Longtin, André
Mathematical modelling has a long history of providing us with useful, succinct descriptions of the natural world. More than mere descriptions, the resulting models provide predictions that can be tested within the framework of the scientific method. But as we seek however to understand progressively more complex systems—especially in interdisciplinary fields like neurophysics—the methods of old don’t always yield the models we want: often they are too crude, or have too many free parameters—either of which makes them difficult to test. This work develops new paths to modelling, meeting complexity with today’s abundance of computational power. In contrast to other work however, which may outright replace the models of old with highly accurate but inscrutable ones, such as neural networks, here we stay firmly in the modelling tradition. By focussing on mechanistic, equation-based models, we extend rather than replace existing modelling capabilities. First we develop methods to learn complex models, where the form of the equations are known from our understanding of biology, but the effective parameters can only be inferred by fitting the entire model to data. These methods further allow to characterize correlations between parameters using Markov chain Monte Carlo (MCMC). However they can also lead to the proliferation of candidate models, which all plausibly fit the data. Therefore in a second part we develop a new approach to model comparison, integrating the principles of scientific induction with the methods of machine learning. The method is based on a novel random walk on the cumulative distribution function of errors; we thereby obtain an empirical modelling discrepancy (EMD) statistic that accounts for epistemic uncertainty on the model, specifically uncertainty under replications of the experiment. Together, these methods can be seen as the basis of a program for expert-driven, computer-enhanced research. And while developed in the context of computational neuroscience, they should be applicable wherever one is concerned with designing and studying mechanistic models, be they models in computational biology, statistical physics, or even econometrics. Les techniques de modélisation mathématique nous ont longtemps fournis des descriptions utiles et succinctes du monde naturel. Plus que de simple descriptions, les modèles ainsi obtenus fournissent des prédictions pouvant être validées à l’intérieur du cadre de la méthode scientifique. En cherchant toutefois à comprendre des systèmes de plus on plus complexes—en particulier dans les domaines interdisciplinaires comme la neurophysique—ces méthodes arrivent parfois à leurs limites: les modèles sont trop simplistes, ou possèdent trop de paramètres indéterminés—et par conséquent peuvent rarement être validés. Ce travail développe de nouveaux moyens de modélisation, répondant à la complexité avec l’abundance de puissance de calcul dont on bénéficie aujourd’hui. À l’inverse d’autres efforts toutefois, il n’est pas question ici de remplacer les modèles conventionnels par d’autres plus précis mais indéchiffrables tel que des réseaux neuronaux. En se concentrant sur des modèles mécanistiques, exprimés à l’aide d’équations, on reste plutôt fermement ancré dans la tradition de modélisation, étendant ses possibilités au lieu de les remplacer. On développe d’abord des méthodes pour apprendre des modèles, où la forme des équations découle de notre connaissance biologique, mais où les paramètres doivent être inférés en adaptant le modèle entier à des données observées. Une fois les paramètres appris, ces méthodes permettent ensuite également de caractériser leurs corrélations à l’aide de Monte-Carlo par chaînes de Markov (MCMC). Toutefois, elles peuvent aussi mener à un prolifération de modèles candidats, chacun ressemblant aux données. C’est pourquoi en seconde partie on développe une nouvelle perspective sur la sélection de modèles, intégrant les principes d’induction scientifique avec les méthodes d’apprentissage automatique. L’approache est basée sur un nouveau processus stochastique sur la distribution cumulative des erreurs; on obtient ainsi une statistique empirique de la divergence du modèle (qu’on nomme EMD, pour empirical modelling discrepancy). Cette statistique tient compte de l’incertitude épistemique du modèle, spécifiquement l’incertitude due aux réplications de l’expérience. Ensemble, ces méthodes peuvent être vues comme formant la base d’un programme de recherche mené par les experts et renforcé par le calcul. Et bien que qu’elles aient été développées dans le contexte de la neuroscience computationelle, elles pourraient être appliquées à tout contexte où l’on conçoit et étudie des modèles mécanistiques—comme la biologie computationelle, la physique statistique, ou même l’économétrie.
From Formal SYMBOLEO Specifications to Secure and Interactive Smart Contract Code
(Université d'Ottawa | University of Ottawa, 2026-05-14) Alfuhaid, Sofana; Amyot, Daniel; Mylopoulos, John; Anda, Amal
Context: Legal contracts have served as the bedrock of business transactions for millennia. They are core to modern supply chains, and their execution can now be automated through the use of i) smart contracts, supported by blockchain technology that safeguards data integrity, and ii) Internet-of-Things technologies to support their monitoring functions. Symboleo is a specification language used to formalize legal contracts, enable property analysis, and generate smart contracts for a permissioned blockchain platform (Hyperledger Fabric). However, automation around resulting smart contracts poses security challenges, particularly regarding who should have access to operate on contract elements. Additionally, how such smart contract should interact with their Cyber-Physical System (CPS) environment, including IoT devices, remains challenging. Purpose: The thesis proposes an architecture to integrate smart contracts, Complex Event Processing (CEP), message brokers, and a blockchain platform (namely Hyperledger Fabric) to support end-to-end Cyber-Physical Smart Contracts (CPSCs). This architecture makes it possible to connect IoT devices with smart contracts (generated using Symboleo) through a CEP engine and a message broker. Additionally, this thesis proposes an access control model, treating all contract elements as resources and ensuring regulated access by designated parties. This model extends the Symboleo ontology and language for legal contracts with new modeling concepts inspired by Role-Based Access Control (RBAC), tailored for the legal contract domain, resulting in SymboleoAC (Symboleo Access Control). SymboleoAC also extends the Symboleo language to handle dynamic contract execution scenario. Methodology: This research follows a Design Science Research methodology, which guides the development and evaluation of the research artifacts. This research is conducted in several iterative steps that are divided into two main phases, one that focuses on theoretical aspects and the other on the design, demonstration, and evaluation of the research artifacts. Contributions: The contributions of this thesis are: • An architectural framework for CPSCs that leverages complementary aspects of CPS and smart contracts; • SymboleoAC, an access control ontology for Symboleo; • An extension of the current Symboleo specification language (syntax and semantics) that supports smart contract requirements, including automation and control actions, access control, and CPS components; • An implementation of the SymboleoAC ontology and semantics into a reusable JavaScript library (SymboleoACJS), together with a tool, SymboleoAC2SC, that generates JavaScript smart contract code with security aspects for a designated platform (Hyperledger Fabric); and • A secure and event-driven SymboleoAC Application Programming Interface (API) that orchestrates the runtime ecosystem connecting IoT sensors, the message broker, the CEP engine, and the blockchain platform. Through extensive and the evaluation of multiple variations of two contract case studies, SymboleoAC (architecture, ontology, and language), along with its associated tools, is shown to be an effective environment for CPSCs, simplifying the design of secure smart contracts and their connections to message brokers, CEP engines, and IoT devices.