Research Repository

LU|ZONE|UL distributes and preserves the scholarly work of LU faculty. It is a space for faculty to support the dissemination of knowledge created at Laurentian.

Electronic Theses and Dissertations (ETD) Repository This section preserves Master's theses and doctoral dissertations accepted at Laurentian University and is a mechanism for making this form of scholarly work widely accessible.

 

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Improving conservation translocation and ex situ breeding techniques for the recovery of a temperate zone rattlesnake
(Laurentian University Library & Archives, 2024-05-01) Choquette,, Jonahtan Daniel; Dr. Jackie Litzgus Dr. Trevor Pitcher
Reintroduction biology is the study and practice of establishing populations of organisms using tools like conservation translocation. An understanding of how to effectively conduct translocations, particularly with snakes, is lacking and there is a need for technique improvement. Systematic literature review, occupancy modeling, artificial hibernation, retrospective analysis of zoo data, and population viability modeling were used to evaluate the effectiveness of in situ and ex situ techniques to inform future translocations with temperate zone snakes. Field work occurred at the Ojibway Prairie Complex and Greater Park Ecosystem in southern Canada with the Eastern Massasauga (Sistrurus catenatus), a rattlesnake in decline across its global range and in need of research into the effectiveness of population management tools. When working with a low detectability species, results showed that evaluating long-term success of translocations at achieving population establishment requires intensive survey efforts to estimate patch occupancy across a study landscape. In the short-term, the invasive and sample- size-limiting technique of radiotelemetry is required for evaluating success of release tactics. A systematic literature review on snake translocations provided evidence for the utility of a suite of translocation tactics for reducing postrelease effects. However, an assessment of ex situ reproductive output coupled with demographic modelling, showed that certain beneficial tactics (e.g., release of captive-reared snakes) can be problematic to implement and sustain, perhaps due to breeding techniques that fail to replicate natural conditions, and with potential impacts to release site fidelity of translocated snakes. Empirical and theoretical guidance were provided for Massasauga recovery by informing eight steps for a successful snake translocation, beginning with the establishment of translocation goals and ending with effectiveness monitoring. Regardless, recovery efforts are hindered by the limitations of existing ex situ breeding techniques, coupled with intensive supplementation efforts required to overcome postrelease effects in situ and to establish a viable wild population over time. Research into alternative ex situ breeding techniques for viperids (e.g., polygamous matings) and effectiveness of beneficial translocation tactics for snakes in general are required. Reintroduction can be a daunting and resource intensive pursuit, echoing the need to stabilize declining populations before they become small.
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Smart mining simulation with an intelligent supervisory agent for automated shovel dig allocation and truck deployment
(Laurentian University Library & Archives, 2023-11-20) Acheampong, Solomon Opoku; Dr. Eugene Ben-Awuah
The mining industry faces a substantial increase in data generation, making real-time knowledge acquisition challenging. Mining data from day-to-day operations from monitoring devices and sensors include the location of a truck or shovel, processing rate, and quantity of material mined. The complexity of decision-making in mining has grown due to intricate mineral deposits and the need for improved productivity. The focus of this thesis is to develop a decision support framework referred to as smart mining architecture (SMA), which leverages the power of simulation and reinforcement learning for understanding mining system interactions and enhancing mining operational performance. The mining operation and activities of mining equipment are simulated and modelled using a discrete event simulation (DES) and an agent-based model (ABM) respectively. The primary objective of this research is to develop an intelligent supervisory agent capable of making smart decisions such as block sequencing, truck-shovel dispatching and equipment maintenance and repairs towards improved operational performance. A reinforcement learning deep quality network (DQN) algorithm is used to develop and train the agent. The aim of the agent is to learn a policy that maximizes the expected return by interacting in real-time with its environment taking smart actions to improve the learning policy. The developed intelligent supervisory agent (ISA) was successfully implemented for a bauxite mine. The model was used to perform block sequencing, truck-shovel dispatching and equipment maintenance and repairs in the SMA. Experimental cases with integrated ISA were compared with experimental cases without the ISA. The output results with ISA maintained consistent production levels in the presence of mining operational uncertainties. Additionally, in the final experiment involving the DES, ABM and ISA, there was 3% increase in total cashflow from the smart management of the short term mine plan and mining equipment deployment as compared to experiments without the ISA. With the introduction of the ISA, fewer workers are required to run the mining operation resulting in financial savings. Fewer workers also mean less interaction between people and equipment leading to improvement in health and safety records and occupational health challenges.
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Enhancing neural mean teacher learning-based emotion-centric model for image captioning
(Laurentian University Library & Archives, 2023-11-09) Piramoon, Majid; Dr. Kalpdrum Passi
Image captioning is a task in computer vision and natural language processing that involves generating a textual description of the content of an image. The goal of image captioning is to create a system that can accurately recognize the objects, attributes, and relationships depicted in an image, and generate a meaningful description of it in natural language, typically in the form of a sentence or short paragraph. One of the state-of-the-art methods that we can use for image captioning is Nemesis: Neural Mean Teacher Learning-based Emotion-centric Speaker. Nemesis is a neural mean teacher learning-based emotion-centric speaker. It is a proposed neural speaker capable of leveraging emotional supervision signals in the caption generation process. Nemesis has been applied to the recently introduced ArtEmis dataset, which is the first large-scale dataset for emotion-centric image captioning, containing 455K emotional descriptions of 80K artworks from WikiArt. In this study, I employed a straightforward but improved version of Self-Critical Sequence Training. By modifying the baseline function choice in the REINFORCE algorithm, I introduced a simple alteration. The updated baseline offers enhanced performance without any additional expenses, when compared to the baseline that utilizes greedy decoding.
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The role of preexisting anisotropies in focusing deformation in an Archean intrusion-related Au deposit: a case study from the Upper Beaver Au-Cu deposit, Ontario, Canada
(Laurentian University Library & Archives, 2023-09-09) Orlóci-Goodison, Ruth; Bruno Lafrance
The ca. 2680 Ma Upper Beaver deposit is an Archean intrusion-related gold-copper deposit located in the southern Abitibi greenstone belt between Kirkland Lake and Larder Lake, Canada. Mineralization is centered on the intermediate Upper Beaver Intrusive Complex which was emplaced in the hanging wall of an extensional listric fault early during Timiskaming basin formation. During subsequent deformation events, alteration, preexisting planar anisotropies, and the orientation and composition of the ore zones enhanced strain partitioning, controlling the development of folds, boudins and fabrics in strained ore zones. Steeply-dipping, sericite-altered ore zones, oriented parallel to cross-stratal dikes, developed a continuous foliation surrounding boudinaged and recrystallized quartz-calcite-anhydrite veins, whereas strong, shallowly-dipping, stratiform, garnet-epidote-amphibole skarnoid ore zones developed a wavy, disjunctive cleavage and deformed mainly by folding. The Upper Beaver deposit can be used as a guide for interpreting the development of structures in similar but more complexly deformed deposits along major deformation zones.
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Diagnosis of pleural mesothelioma using machine learning
(Laurentian University Library & Archives, 2023-10-26) Abejide, Olaoluwa Julianah; Dr. Kalpdrum Passi
Mesothelioma is cancer that develops in the pleura. The most common cause of this disease is contact with asbestos. Patients with mesothelioma have a better chance of surviving if they are diagnosed quickly. This study utilizes a variety of machine learning to enhance pleural mesothelioma diagnosis. The possibility of misclassification was decreased by extracting features from a preexisting dataset. SVM, Decision Trees, and Random Forests are only a few machine learning classifiers trained using essential and foundational features. Accuracy, precision, recall, and F1-score were just a few measures used to evaluate these classifiers' performance in cross- validation. SVM demonstrated excellent accuracy, precision, recall, and F1-score when classifying individuals as either healthy or having mesothelioma. The results show the potential of machine learning techniques for early diagnosis of pleural mesothelioma. Machine learning algorithms improve diagnosis accuracy and turnaround time, improving patient outcomes. Using the results of this research, a fully automated technique for diagnosing mesothelioma might be developed, allowing clinicians more time to provide better care for their patients.