Optimal and global autonomous navigation in environments with convex obstacles

Motion planning for autonomous navigation in unknown environments cluttered with obstacles is a fundamental challenge in robotics, requiring efficient, safe, and reliable strategies for path planning. This thesis introduces two novel autonomous navigation strategies for vehicles operating in static, unknown n-dimensional environments clut- tered with convex obstacles. The first strategy proposes a continuous feedback controller that steers a vehicle safely to a target destination in a quasi-optimal manner within a “sphere world,” where each obstacle is enclosed by a sphere-shaped boundary. Under this approach, the robot avoids obstacles by navigating along the shortest path on the sur- face of the cone enclosing the obstacle and proceeds directly toward the target when no obstacles obstruct the line of sight. This controller guarantees almost global asymptotic stability in two-dimensional (2D) environments under specific obstacles configurations. An extension of this method is also developed for real-time navigation in unknown, static 2D environments with sufficiently curved convex obstacles, maintaining the same stability guarantees. Simulation and experimental results demonstrate the practical effectiveness of this approach in navigating real-world environments. While the first strategy ensures almost global asymptotic stability only under specific conditions related to the obstacles configuration and for 2D environments, the second strategy aims to provide a more robust solution with stronger stability guarantees. This second strategy introduces a hybrid feedback controller designed to navigate a vehicle in static n-dimensional Euclidean spaces cluttered with spherical obstacles. This approach ensures safe convergence to a predefined destination from any initial position within the obstacle-free workspace while optimizing obstacle avoidance. A novel switching mecha- nism is proposed to alternate between two operational modes: the motion-to-destination mode and the obstacle-avoidance mode, ensuring global asymptotic stability regardless of the obstacles’ configuration. Numerical simulations in both known and unknown 2D and 3D environments, along with experimental validation in a 2D setting, demonstrate the effectiveness the proposed approach. These strategies provide robust solutions for autonomous navigation in static, un- known environments, contributing to the advancement of safe, efficient, and optimal motion planning techniques for robotic systems in complex, obstacle-laden spaces

Ecological drivers of fish metacommunity structure in boreal shield lakes

Fish community composition in freshwater lakes is shaped by a range of biotic and abiotic factors, including environmental conditions, species interactions, and spatial connectivity between waterbodies. While aquatic community ecology studies historically treated lakes as isolated systems, recent research has increasingly embraced a metacommunity framework, integrating spatial connectivity with environmental and biological predictors of community composition. Despite this shift, few studies have thoroughly examined the relative roles of spatial connectivity, environmental factors, and species interactions in shaping lake fish communities. To address this gap, I conducted a study across 81 lakes distributed within two quaternary watersheds at the IISD Experimental Lakes Area in northwest Ontario. Using Joint Species Distribution Modeling (JSDM) alongside spatial eigenvector mapping techniques—Asymmetric Eigenvector Mapping (AEM) and Moran’s Eigenvector Mapping (MEM)—drivers of fish community composition were investigated. Results indicate that spatial variables—specifically lake connectivity, stream flow direction, and the maximum gradient along connecting streams—are primary drivers of fish metacommunity composition. In presence-absence models, these spatial factors explained more variation than environmental variables and species co-occurrence patterns (potentially reflecting species interactions). Conversely, relative abundance models (conditional on presence) performed poorly across all ecological models evaluated. These findings provide valuable insights into the role of spatial connectivity relative to other factors in shaping fish community structure on a presence-absence basis, emphasizing the importance of applying a metacommunity approach in community analyses

Being, becoming, and belonging: a constructivist grounded theory study describing the process of social norm formation of nurses working in groups

COVID-19 exacerbated the worldwide shortage of nurses. The Registered Nurses Association of Ontario reported that the number of vacancies among registered nurses in the province has more than quadrupled since the beginning of 2016 and has more than doubled since the start of the pandemic. Reasons identified by researchers contributing to nurses leaving the profession have included lack of support from peers and management, little input from nurses into their practices, increasingly heavy patient loads, and increasing patient acuity. The purpose of this constructivist grounded theory study was to develop a theory to explain the process of social norm formation of nurses working in groups and to identify the factors limiting or facilitating their development. Group social norms, or rules, contribute to the environment in which nurses practice. [...

An improved semi-supervised learning framework for Image semantic segmentation

Traditional supervised learning methods depend heavily on labeled data, which is both costly and time-intensive to acquire. Self-supervised learning approaches present a promising alternative to supervised learning, enabling the utilization of unlabeled data. Thus, this research aims to build an advanced semi-supervised semantic segmentation model that strikes a balance between selfsupervised and fully supervised paradigms for visual perception applications in an autonomous driving environment. In this direction, the thesis is structured into three distinct phases, beginning with self-supervised image classification and progressing toward bi-level image segmentation, ultimately culminating in the development of an advanced semantic segmentation model. Initially, this research employs a simple contrastive learning framework (SimCLR) to classify medical images, specifically focusing on monkeypox diagnosis from skin lesion images, while integrating a federated learning (FL) framework to ensure data privacy. Monkeypox classification is a simple binary classification task and the dataset found for this problem, in this thesis, is very manageable on the computational resources that were available at the onset of this research. It paved the way to grasp non-supervised learning basics and explore how they differ from traditional supervised learning methods. The subsequent phase involves the development of an efficient convolutional neural network (CNN) with an attention mechanism, applied to the bi-level segmentation task of road pavement crack detection. Similar to the Monkeypox classification, this is also a binary classification task, but at pixel-level, i.e., it is a two-way semantic segmentation problem. Hence, the number of samples found in the relevant datasets is once again manageable on the computational resources available during the research. [...

Evaluating the application of LiDAR to measure wildland fire depth of burn In the Canadian boreal forest.

This study evaluates the accuracy of Light Detection and Ranging (LiDAR) technology in measuring the depth of burn (DoB) resulting from wildland fires in the Canadian boreal forest. An analysis of the correlation between LiDAR and ground truth DoB measurements was conducted to determine the accuracy of the LiDAR measurements. Initial results revealed errors within the spatial alignment of the pre- and post-burn LiDAR data. Adjustments for spatial discrepancies using an offset approach were implemented; however, a poor correlation between measurements persisted. These findings indicate LiDAR is not an effective method for measuring the DoB in complex landscapes such as the boreal forest. Despite these findings, the study strongly advocates for the continuation of research in this area to increase confidence in these results. Recommendations for future research include increasing the number and diversity of sampling locations and refining ground sampling and LiDAR data processing techniques to enhance measurement accuracy in complex forest landscapes

Drying of softwood kraft lignin and its cationic derivatives

Our dependence on petroleum-based materials comes with significant environmental challenges such as environmental pollution, including oil spills and air pollution. Furthermore, the utilization of petroleum-based products leads to the emission of greenhouse gases, which contributes to climate change. Additionally, many petroleum-based products, such as plastics, are not biodegradable and accumulate in the environment, causing harm to wildlife and ecosystems. For these reasons, researchers continue to seek out sustainable alternatives to petroleum-based materials. Lignin is a promising starting material for producing sustainable and environmentally compatible chemistries. Lignin is an abundant and sustainable resource. Organic synthesis and polymerization reactions are utilized as effective methods for derivatizing lignin and tailoring it for application in various industries. After synthesis and purification, drying is a crucial final step. Generally drying conditions should be carefully selected to introduce minimal changes to the properties of the synthesized products. Furthermore, there have been reports of lignin’s sensitivity to heat, stimulating condensation reactions which alter the properties of the sample. There are no comprehensive studies that investigate the topic of drying of lignin derivatives. In this work, the objectives were to derivatize lignin in two ways via: 1) grafting reaction to create a low molecular weight cationic lignin derivative and 2) polymerization to create a high molecular weight cationic lignin derivative. [...

Symptoms of premenstrual dysphoric disorder and cycle phase are associated with enhanced facial emotion detection: An online cross-sectional study

The authors wish to thank Chyenne Panetta and Nandini Parekh who helped with the organization and sorting of the data for the FEDT. Some of the data in this paper were reported in the MA thesis of the first author.Background: Premenstrual dysphoric disorder is a depressive disorder affecting 5%–8% of people with menstrual cycles. Despite evidence that facial emotion detection is altered in depressive disorders, with enhanced detection of negative emotions (negativity bias), minimal research exists on premenstrual dysphoric disorder. Objectives: The goal of this study was to investigate the effect of premenstrual dysphoric disorder symptoms and the premenstrual phase on accuracy and intensity at detection of facial emotions. Design: Cross-sectional quasi-experimental design. Method: The Facial Emotion Detection Task was administered to 72 individuals assigned female at birth with no premenstrual dysphoric disorder (n=30), and provisional PMDD (n=42), based on a retrospective Diagnostic and Statistical Manual of Mental Disorders—Fifth Edition-based measure of premenstrual dysphoric disorder. Facial emotion detection was examined both irrespective of menstrual cycle phase, and as a function of premenstrual phase (yes, no). The task used neutral-to-emotional facial expression morphs (15 images/morph). Participants indicated the emotion detected for each image within the progressive intensity morph. For all six basic emotions (sad, angry, fearful, happy, disgust, and surprise), two scores were calculated: accuracy of responses and the intensity within the morph at which the correct emotion was first detected (image number). Results: Individuals reporting moderate/severe symptoms of premenstrual dysphoric disorder had more accurate and earlier detection of disgust, regardless of cycle phase. In addition, those with provisional premenstrual dysphoric disorder detected sad emotions earlier. A premenstrual dysphoric disorder group×cycle phase interaction also emerged: individuals reporting premenstrual dysphoric disorder symptoms were more accurate at detecting facial emotions during the premenstrual phase compared to the rest of the cycle, with a large effect size for sad emotions. Conclusion: The findings suggest enhanced facial emotion processing in individuals reporting symptoms of premenstrual dysphoric disorder, particularly for sadness and disgust. However, replication is required with larger samples and prospective designs. This premenstrual dysphoric disorder premenstrual emotion detection advantage suggests an adaptive cognitive mechanism in premenstrual syndrome/premenstrual dysphoric disorder, and challenges stigma surrounding premenstrual experiences

Impact of urbanization on McVicar Creek, Thunder Bay

Urbanization has significantly altered natural ecosystems, particularly impacting waterbodies like streams and creeks. In Thunder Bay, Ontario, the urbanization of McVicar Creek has led to increased impermeable surfaces and reduced riparian shading, resulting in changes to stream characteristics and water temperature dynamics. This thesis investigates the adverse effects of urbanization on McVicar Creek, with a focus on water temperature variations as a key indicator. Through the collection and analysis of water temperature data from urban and non-urban study sites, this research aims to assess the impact of urbanization on stream thermal regimes. Results indicate elevated water temperatures in urbanized segments of McVicar Creek compared to rural areas, suggesting the influence of an urban heat island within the city of Thunder Bay. Additionally, the study reveals significant differences in stream depth and width between urban and non-urban sites, highlighting the morphological alterations induced by urbanization. These findings underscore the importance of stream restoration projects and long- term monitoring to mitigate the adverse effects of urbanization on stream ecosystems. By understanding the impacts of urbanization on waterbodies, policymakers and environmental managers can develop effective strategies to protect and rehabilitate urban streams, ensuring the health and sustainability of aquatic ecosystems in urban environments

Energy density of fish within an aquaculture experiment

Understanding how small-bodied fish are affected by aquaculture is important to help complete the picture on how aquaculture affects all levels of the ecosystem. I analysed the energy density for small-bodied fish in the presence of aquaculture. The experiment was done in a whole lake ecosystem within the boreal shield. This study focused specifically on finescale dace within two similar lakes; Lake 375 had aquaculture operating for 5 years and Lake 373 was monitored as a reference lake. Aquaculture likely had a positive impact on the energy density of finescale dace as they had access to an increased food source. While the energy density of minnows was higher in Lake 375 than Lake 373, there was a higher overwinter mortality rate in Lake 375. Based on findings presented here and from information reported elsewhere, I conclude that previously reported minnow overwinter mortality was largely due to an increase of predation of minnows from lake trout, rather than due to energetic deficits. While aquaculture appears to benefit the minnows where they displayed increased energy density and population densities, overwinter mortality may cause the minnow population densities to become unpredictable and volatile with an aquaculture operation

Experimental investigation of effect of cement content and sulphate concentration on loading rate-dependent fracture behaviour of CPB under Mode I, II, and III loading conditions

Cement paste backfill (CPB) technology is becoming the standard mine backfilling approach in the mining industry as an environmentally friendly and cost-effective way to manage tailings. Most importantly, CPB plays a critical role in ground support to the surrounding rock mass to ensure the safe and effective operation of the mine. After placement into the mined-out voids, the CPB structure is subjected to complex loading conditions. Due to the dependency of mechanical behaviour on the loading rate, CPB may demonstrate distinctive response and fracture failure characteristics under field loading conditions. However, previous research has primarily concentrated on traditional geomechanical behaviours, ignoring the impact of loading rate on the fracture behaviour of CPB, which significantly influences the assessment of mechanical behaviour and performance of in-stope CPB. Meanwhile, as a cementitious composite, the chemical factors, including cement content and sulphate concentration, dominate the evolution of the mechanical properties of CPB. Therefore, it is of theoretical and practical importance to investigate the effects of cement content and sulphate concentration on CPB's loading rate dependent fracture behaviour. The research aims to evaluate the loading rate dependent fracture behaviour and properties of CPB under different loading conditions. [...