TECTONIC EVOLUTION OF THE BAIE VERTE MARGIN, NEWFOUNDLAND

Located along the Early Paleozoic Laurentian continental margin in Newfoundland, the Baie Verte Margin tectonistratigraphy and tectono-metamorphic evolution have been controversial for decades. Here, the results of a detailed field, petrological and geochronological study are presented, where Baie Verte Margin is subdivided into three tectono-metamorphic units separated by tectonic contacts: the East Pond Metamorphic Suite (EPMS) basement, EPMS cover, and the Fleur de Lys Supergroup (FdLS). Each unit exhibits a distinct metamorphic and structural evolution recorded during the subduction, exhumation, and post-collisional history of this ancient margin. The combination of thermodynamic modelling, petrochronology, and structural analysis provided insights into the P-T-t-d paths of the studied units, allowing a better understanding of their role during the evolution of the Taconic subduction system. High-pressure (HP) to ultra-high-pressure (UHP) conditions were reached between 483 and 475 Ma during the D1 phase, with the EPMS cover recording eclogite-facies metamorphism at ~2.8 GPa and 620°C. Subsequent decompression resulted in a β-shaped pressure-temperature-time (P-T-t) path, with near-isothermal decompression to ~2 GPa and heating to 860°C during exhumation. A multi-stage exhumation model is proposed for the EPMS eclogites: 1) buoyant rise through a low-density mantle wedge and 2) subsequent ascent at shallower crustal levels, facilitated by external tectonic forces and slab break-off, as evidenced by Late Taconic magmatism. While the EPMS cover re-equilibrated at UHP conditions, the EPMS basement and FdLS experienced decompression and Barrovian metamorphism during late-D1, indicating decoupling of the units during this stage. Coupling between the units occurred along a D2 shear zone during retrograde metamorphism, spanning 475–452 Ma. Two exhumation scenarios are proposed to explain the tectonic evolution of the margin: (i) Following late D1 detachment, the EPMS basement and FdLS were exhumed to crustal levels while the EPMS cover was subducted deeper into the mantle. Tectonic extrusion along D2 shear zones, potentially aided by melt weakening, then emplaced the EPMS cover between the two units. (ii) Alternatively, sequential detachment occurred from the top to the bottom of the slab, resulting in deeper subduction of lower units, followed by their exhumation through back-folding and crustal wedge thrusting. The Silurian F3 folding deforms both D1-2 structures in each unit and the D2 shear zones that bound them, suggesting that the continental wedges, which recorded different tectono-metamorphic paths after early D1, were juxtaposed before the onset of deformation associated with the Salinic Orogeny. Later deformation phases, D4 and D5, are probably related to tectono-metamorphic activity related to the Acadian and Neo-Acadian orogenies. This research improves our understanding of the dynamic tectono-metamorphic evolution of the Baie Verte Margin, emphasizing the role of fluids, thermal perturbations, and deformation in driving metamorphic reactions, and exhumation. The findings contribute to understanding the mechanisms controlling HP-UHP terrain evolution in subduction zones and highlight the complex interactions between subduction, exhumation, collision, and magmatism throughout the Taconic orogeny

Location for Profit Maximization: Applications to On-Demand Warehousing and Pilotless Air Cargo Transportation

Modern logistics systems face increasing complexity due to the rapid growth of e-commerce and emerging technologies, particularly in shared warehousing platforms and air freight delivery. Accordingly, advanced approaches are needed to effectively address large-scale network design problems that prioritize profit-oriented objectives. This dissertation develops mathematical models and effective solution methodologies to optimize profitability in on-demand warehousing and pilotless air cargo transportation networks. In particular, the thesis introduces decomposition methods and heuristic algorithms to solve facility location problems arising in these applications with a focus on profit-maximization. First, a Lagrangian relaxation framework combined with a local search heuristic is used to solve a multi-period profit-maximizing facility location model in on-demand warehousing, accounting for price-sensitive demand and dynamic allocation. Next, for pilotless air cargo transportation, two network design models are developed and solved using a branch-and-price and genetic algorithm enhanced with score-based labeling and a hybrid heuristic to optimize business profit, hub placement, and airplane routing. The findings provide practical insights for enhancing profitability and operational responsiveness in both on-demand warehousing and pilotless air cargo logistics

Impacts of temperature variation on duckweed population growth and distribution in a changing climate

Understanding the impacts of climate change on aquatic plants involves examining how temperature fluctuation patterns influence their temperature-dependent vital rates and distribution. Duckweeds, small aquatic plants with both economic significance and ecological concern, can pose challenges due to overgrowth and the spread of invasive species. The impact of climate-induced temperature changes on aquatic plants remains poorly understood, as many studies use constant conditions that do not account for natural variability in temperature. Research focused on increased average temperatures has shown general ectotherm responses tied to geographic location, such as enhanced growth in temperate regions. However, when temperature fluctuations are considered, responses differ from those under constant conditions due to nonlinear and asymmetrical thermal performance. Increased autocorrelation, with prolonged sequences of unusually high or low temperatures, can affect population growth rates, while nighttime warming alters diel temperature variation and potentially influences time-sensitive processes like photosynthesis and respiration. This thesis investigates the thermal performance and distribution of duckweed species under varying temperature regimes associated with climate change, incorporating both controlled experiments and predictive modeling. The second chapter uses a Maxent species distribution model to predict the potential range expansion of Landoltia punctata (dotted duckweed), an invasive, herbicide-resistant species. Habitat suitability is modeled under current and future climate scenarios, using satellite-derived water temperature data and constraining model features to match the shape of thermal performance curves obtained from laboratory experiments. Results indicate high suitability for this species in Western Europe and Southern Canada, with the Great Lakes region becoming increasingly suitable in the future due to climate warming. These projections underscore the importance of climate-informed management strategies to mitigate the ecological impact of invasive species. The third chapter investigates how diel temperature variability and climate change affect the reproduction of Lemna minor (common duckweed) during spring and summer. Experimental results highlight the importance of temperature variance as opposed to the timing of warming. While increased mean spring temperatures enhance duckweed performance, reduced temperature variance during high summer temperatures in regions such as Canada helps mitigate the negative impacts of otherwise excessively hot fluctuating conditions. These findings emphasize the varying effects of climate change on duckweed's thermal performance across different seasons. The fourth chapter examines the effects of temperature autocorrelation on both common and dotted duckweed reproduction and survival. Experiments show that strongly autocorrelated sequences result in mortality due to heat stress when hot temperature sequences begin with elevated heat. In contrast, autocorrelation has limited impacts under cooler average conditions, likely due to slower physiological responses. These findings align with broader predictions of increased extinction risks for ectotherms under persistent and extreme temperature patterns caused by climate change. This work is a step towards a more realistic understanding of aquatic plant responses to climate change by considering thermal performance responses, diverse temperature fluctuation patterns, and water temperatures. Our results can be used in population dynamics models to make more realistic predictions of climate change responses. The experimental and modeling findings in this thesis advance our understanding of aquatic plant responses to climate change and support the development of informed strategies to manage their ecological impacts and sustainable production in a warming world

Pre-treatment direct costs for people with tuberculosis during the COVID-19 pandemic in different healthcare settings in Bandung, Indonesia

© 2025 Lestari et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.The tuberculosis (TB) program was massively disrupted due to the COVID-19 pandemic, which may have impacted on an increase in costs for people with TB (PWTB) and their households. We aimed to quantify the pre-treatment out-of-pocket costs and the factors associated with these costs from patients' perspective during the COVID-19 pandemic in Bandung, Indonesia. Adults with pulmonary TB were interviewed using a structured questionnaire for this cross-sectional study recruiting from 7 hospitals, 59 private practitioners, and 10 community health centers (CHCs) between July 2021 to February 2022. Costs in rupiah were converted into US dollars and presented as a median and interquartile range (IQR). Factors associated with costs were identified using quantile regression. A total of 252 participants were recruited. The median total pre-treatment cost was $35.45 (IQR 17.69-67.62). The highest median cost was experienced by participants from private hospitals ($54.51, IQR 29.48-98.47). The rapid antigen and PCR for SARS-CoV-2 emerged as additional medical costs among 26% of participants recruited in private hospitals. Visiting >-6 providers before diagnosis ($38.40 versus$26.20, p < 0.001), presenting first at a private hospital ($50.68, p< 0.05) and private practitioners ($34.97, p < 0.05), and being diagnosed in the private health sector ($39.98 versus$20.30, p < 0.05) were significantly associated with higher pre-treatment costs. PWTB experienced substantial out-of-pocket costs in the process of diagnosis during the COVID-19 pandemic despite free TB diagnosis and treatment. Early detection and identification play an important role in reducing pre-diagnostic TB costs.Bill & Melinda Gates Foundation, INV-022420

Model Predictive Control for Systems with Partially Unknown Dynamics Under Signal Temporal Logic Specifications

Autonomous systems are seeing increased deployment in real-world applications such as self-driving vehicles, package delivery drones, and warehouse robots. In these applications, such systems are often required to perform complex tasks that involve multiple, possibly inter-dependent steps that must be completed in a specific order or at specific times. One way of mathematically representing such tasks is using temporal logics. Specifically, Signal Temporal Logic (STL), which evaluates real-valued, continuous-time signals, has been used to formally specify behavioral requirements for autonomous systems. This thesis proposes a design for a Model Predictive Controller (MPC) for systems to satisfy STL specifications when the system dynamics are partially unknown, and only a nominal model and past runtime data are available. The proposed approach uses Gaussian Process (GP) regression to learn a stochastic, data-driven model of the unknown dynamics, and manages uncertainty in the STL specification resulting from the stochastic model using Probabilistic Signal Temporal Logic (PrSTL). The learned model and PrSTL specification are then used to formulate a chance-constrained MPC. For systems with high control rates, a modification is discussed for improving the solution speed of the control optimization. In simulation case studies, the proposed controller increases the frequency of satisfying the STL specification compared to controllers that use only the nominal dynamics model. An initial design is also proposed that extends the controller to distributed multi-agent systems, which must make individual decisions to complete a cooperative task

From disaster recovery to whole-of-society resilience: The impact of the 2021 British Columbia atmospheric rivers event on flood risk management policy and governance

Flooding poses significant risks to the safety, well-being, and long-term security of many Canadian communities. In recent years, extreme weather events, as a result of a changing climate, have cost Canadians billions in insured and uninsured losses annually, and such losses do not encapsulate the various social, ecological, and health impacts that are difficult to quantify. In addition to climate change, misaligned land-use planning, intensified development in floodprone areas, fragmented risk governance, gaps in funding and policy, and an over-reliance on protective structures continue to place many Canadians in harm’s way, while creating barriers for proactive adaptations at the watershed scale. Major disasters, like the 2021 atmospheric rivers floods in British Columbia, underscore the need for transformative flood risk management [FRM] policy and governance by highlighting the systemic drivers of flood risk, namely a FRM system that was never designed to withstand the dynamic realities of the present day. Such focusing events—relatively rare, sudden, and impactful events like disasters—are often critical in generating significant public interest around a focal issue, garnering political will to advance policy agendas, and enabling governance actors to advocate for policy reform. In the post-disaster landscape, coalitions of policy actors can seek to leverage these emergent ‘windows of opportunity’ to advance a paradigm shift in how various public issues, like disaster risk reduction and climate change adaptation, are understood and managed, who is involved in decision-making processes, and what solutions are considered socially-acceptable and politically-feasible. Actors are most likely to be successful in advancing agenda items if enabled by the institutional environments that policy processes are embedded within, and if there is an existing foundation of collaboration among others within the broader policy community. This research, utilizing a case study of a major Canadian flood disaster, evaluates the ways in which policy champions, advocacy coalitions, and institutional actors have sought to leverage existing relationships, prior learnings, and post-disaster momentum to advance shifts in FRM policy and governance at the local, regional, and provincial scales. Semi-structured key informant interviews provide insights into how the disaster manifested as a focusing event, what enabling conditions contributed to the creation of a window of opportunity for policy change, and how recent shifts in British Columbia’s flood governance and policy regimes have been shaped by longer-term institutional developments and interjurisdictional partnerships. This research illustrates the transformational nature of adaptive learning and multi-scalar governance, and is intended to assist FRM decision-makers, policymakers, and practitioners in advancing resilience

Using Artificial Intelligence for Some Activity Recognition and Anomaly Identification Using a Multi-Sensor Based Smart Home System

Ambient Assisted Living (AAL) research frequently contends with limitations including reliance on supervised data, lack of personalization and interpretability, and evaluations in artificial laboratory settings. This study aimed to address these gaps by developing an unsupervised, personalized, and interpretable AAL system using low-cost sensors for long- term, real-world activity recognition and behavioural anomaly detection. A multi-modal sensor network (including contact, vibration, outlet, air quality sensors) was deployed in a single participant’s apartment for over 90 days. Primarily unsupervised machine learning techniques, augmented with interpretability methods (SHAP), were used to identify key ac- tivities (cooking, couch-sitting, showering) and detect personalized behavioural deviations. Minimally supervised approaches for showering detection were also accurately achieved using humidity data to address the shortcomings of unsupervised showering model. More importantly, the system effectively identified interpretable anomalies demonstrating the model’s capability to learn the individual’s normal behaviour in the home and identify anomalies, representing significant deviations from the participant’s established routines. In addition, the model was also able to be interpretable that allowed for the participant to understand why each anomaly occured. This study confirms the feasibility of leveraging unsupervised, interpretable methods with affordable sensors for personalized, ecologically valid AAL, significantly reducing labelling dependence and enhancing system trustworthi- ness for scalable, unobtrusive health monitoring

Development of Novel Surface Finishing Processes for Additively Manufactured Metal Parts

Poor surface quality is one of the drawbacks of metal parts made by various additive manufacturing (AM) processes. They normally possess high surface roughness and different types of surface irregularities. Post-processing operations are needed to reduce the surface roughness to have ready-to-use parts. Among all the surface treatment techniques, electrochemical surface finishing methods have the highest finishing efficiency. However, there are challenges with electropolishing in terms of reducing surface roughness of metals parts made via AM. Firstly, parts made with AM have both small-scale surface roughness and large-scale surface waviness. Electropolishing is only suitable for the reduction of micro-scale surface roughness while it is difficult to use the method to remove meso- to macro-scale surface waviness. In addition, it is still challenging to use electropolishing to reduce the surface roughness of internal channels of additively manufactured parts, benefiting from the promising feature of AM to produce parts with complex internal geometries. Finally, how to improve process sustainability is another question that needs to be addressed, since hazardous and corrosive chemicals are always used for the technique. To address the aforementioned problems, novel approaches were developed, incorporating both modeling and experimental investigations. Analytical and numerical models were constructed to explore the mechanisms of electropolishing and to understand the surface evolution during the process. The results offer valuable insights that can guide the design of experiments and foster the development of novel processes. The first experimental study focuses on using hybrid surface finishing technique to reduce meso-/macro- surface waviness. A novel surface finishing technique combining electrochemical polishing, ultrasonic cavitation and abrasive finishing was designed. Experiments were conducted on both electropolishing and hybrid finishing and the results were compared. While similar optimal arithmetic mean height values (Sa ≈ 1 μm) are achieved for both processes, the arithmetic mean waviness values (Wa) obtained from hybrid finishing are much less than those from sole electropolishing after the same processing time. The second experimental investigation aims at electropolishing internal channels. For doing this, a novel cathode tool was invented and fabricated using polymer 3D printing. Electropolishing was conducted on both straight and curved channels with different curvatures. Preliminary experiments demonstrated a maximum surface roughness Sa reduction, from 10.86 ± 0.50 μm to 1.44 ± 0.46 μm. Apart from this, electropolishing failure mechanisms were explained and design optimization was conducted through numerical simulation. The investigations show that the method is promising in reducing surface roughness of internal channels. In addition, experimental trials were also conducted to improve the sustainability of the surface finishing processes, including using greener electrolytes for electropolishing, and developing shear thickening polishing. Both alcohol-salt electrolyte system and deep eutectic solvent electrolyte were investigated, demonstrating effective surface roughness reduction. Shear thickening polishing using the corn starch slurry was also explored. In spite of some promising results, the process was not repeatable due to numerous influencing factors

“AnnoTools”: Extending AnnoTree and AnnoView for Database-Wide Genome Annotation, Visualization, and Comparison

Genomic analysis has revolutionized our understanding of the biology and evolutionary history of bacterial and archaeal microorganisms, leading to numerous applications in biotechnology, medicine, and environmental sciences. One of the fundamental aspects of genomic analysis is protein functional annotation, which involves assigning biological functions to protein-coding sequences identified within genomes. These annotations are widely used to support analyses, such as examining gene or function distributions across the tree of life and comparing gene neighborhoods across taxa. By combining these analyses, researchers can comprehensively explore gene functions and the mechanisms of given genes or gene clusters. In this thesis, I will introduce a pipeline that supports genomic analysis. The project consists of three parts: data annotation, visualization, and the language model. The first part of the pipeline is the generation of protein function annotations. Raw protein sequence data is downloaded from the Genome Taxonomy Database (GTDB) and submitted to two tools: Kofamscan and DIAMOND. Kofamscan assigns KEGG ORTHOLOGY IDs to each input sequence, while DIAMOND assigns Uniref IDs, which are then mapped to InterPro IDs. Combining these IDs provides comprehensive and reliable annotations. The data is filtered for quality and stored on a remote server as an annotation database for further analysis. The second part of the pipeline involves updating two user-friendly, web-based visualization tools, AnnoTree and AnnoView, which utilize the annotation database. AnnoTree displays the distribution and taxonomy of different protein annotations across GTDB using a tree of life representation, offering insights into biological and evolutionary patterns through species phylogenies and supporting genome-wide co-occurrence analysis. AnnoView focuses on comparing and exploring gene neighborhoods, identifying functionally related genes clustered together in genomes as "gene clusters," thus emphasizing window-based co-occurrence analysis. The new annotation database not only provides more comprehensive and accurate annotations, enhancing the databases that both visualization tools rely on, but also extends their functionalities for fast data retrieval and new features. The last part of the pipeline involves the application of the Word2Vec language model, which treats genome contigs as sentences in natural language and trains the model using the annotation database. After training, the updated model can encode each annotation from a specific protein family into high-dimensional vectors with continuous number, allowing researchers to explore annotations that share similar genomic contexts. This allows protein functions prediction based on this comparative gene neighborhood analysis. Finally, I will use one protein domain in the Type VI Secretion System (T6SS) as a case study. T6SS is a cell envelope-spanning machine that translocates toxic effector proteins into eukaryotic and prokaryotic cells. Besides the conserved essential core components, there are various effector and accessory proteins in the system. Some proteins are annotated as Domains of Unknown Function (DUF) and are poorly explored. In this case, I will focus on PF20598 (DUF6795), which shares a similar genomic context with one of the T6SS proteins. Using the visualization tools AnnoTree and AnnoView, I will demonstrate that this DUF is part of the T6SS cluster, supporting the hypothesis that it may function as an adaptor protein in T6SS. In summary, the AnnoTools pipeline integrates all components to enhance comparative genomic analysis with a large-scale annotation database. The user-friendly web-based tools enable researchers to visualize data both genome-wide and at a window-based scale. The ultimate goal of this thesis is to provide researchers with a comprehensive and easy-to-use method for predicting functions of genes or gene clusters of interest

Reweighted Eigenvalues: A New Approach to Spectral Theory beyond Undirected Graphs

We develop a concept called reweighted eigenvalues, to extend spectral graph theory beyond undirected graphs. Our main motivation is to derive Cheeger inequalities and spectral rounding algorithms for a general class of graph expansion problems, including vertex expansion and edge conductance in directed graphs and hypergraphs. The goal is to have a unified approach to achieve the best known results in all these settings. The first main result is an optimal Cheeger inequality for undirected vertex expansion. Our result connects (i) reweighted eigenvalues, (ii) vertex expansion, and (iii) fastest mixing time [BDX04] of graphs, similar to the way the classical theory connects (i) Laplacian eigenvalues, (ii) edge conductance, and (iii) mixing time of graphs. We also obtain close analogues of several interesting generalizations of Cheeger’s inequality [Tre09, LOT12, LRTV12, KLLOT13] using higher reweighted eigenvalues, many of which were previously unknown. The second main result is Cheeger inequalities for directed graphs. The idea of Eulerian reweighting is used to effectively reduce these directed expansion problems to the basic setting of edge conductance in undirected graphs. Our result connects (i) Eulerian reweighted eigenvalues, (ii) directed vertex expansion, and (iii) fastest mixing time of directed graphs. This provides the first combinatorial characterization of fastest mixing time of general (non-reversible) Markov chains. Another application is to use Eulerian reweighted eigenvalues to certify that a directed graph is an expander graph. Several additional results are developed to support this theory. One class of results is to show that adding $\ell_2^2$ triangle inequalities [ARV09] to reweighted eigenvalues provides simpler semidefinite programming relaxations, that achieve or improve upon the previous best approximations for a general class of expansion problems. These include edge expansion and vertex expansion in directed graphs and hypergraphs, as well as multi-way variations of some undirected expansion problems. Another class of results is to prove upper bounds on reweighted eigenvalues for special classes of graphs, including planar, bounded genus, and minor free graphs. These provide the best known spectral partitioning algorithm for finding balanced separators, improving upon previous algorithms and analyses [ST96, BLR10, KLPT11] using ordinary Laplacian eigenvalues