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Open Access
Rethinking Environmental Obligations in Corporate Insolvencies: What New Role for Lenders?
(2024-04-18) Ebegbodi, Daniel Onyeje; Stewart, Fenner; Wilson, Daniel; Nikolaou, Nickie
Abstract Environmental reclamation obligations are statutory mechanisms designed to regulate environmental protection by corporate entities. Bankruptcy laws on the other hand are meant to offer insolvent corporations an opportunity to reorganize their affairs, satisfy creditors claims and make a fresh start. In practice, the application of bankruptcy laws can undermine key environmental reclamation objectives, leading many to ask whether a corporation undergoing restructuring with significant outstanding environmental reclamation obligations should be able to commence bankruptcy proceedings to satisfy creditors’ claims? By employing the doctrinal and comparative research methodologies, this research interrogates that inquiry. It argues that, despite the importance of bankruptcy protection for corporations undergoing financial distress, environmental protection should be paramount. Although sustainable finance (SF) instruments have been deployed by banks to enable creditors to mitigate environmental concerns in their investments, the persistent recurrence of environmental reclamation issues in the oil and gas sector particularly during insolvencies, underscores the need for financial investors to strengthen their investment policies to reflect best practices providing the desired protection for the environment. The research finds that, although SF and environmental, social and governance (ESG) approaches, are commendable, they are insufficient in instilling adequate regulatory impact on the environment compared to judicial control offered by the courts. The thesis concludes that whilst judicial control mechanism is not without concerns, with government’s deliberate financial policy and judicial control to complement SF and ESG efforts, ESG and SF mechanisms can be strengthened to compel greater significant influence on best practices in lending.
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Open Access
Assessing Carbon Dioxide Euthanasia in Laboratory Rodents
(2024-04-22) Merenick, Dexter Reece; Pang, Daniel SJ; Oliver, Vanessa L; Knight, Cameron
Overdose of carbon dioxide gas (CO2) is a commonly performed euthanasia method for rodents; however, CO2 exposure activates nociceptors in rats and is reported to be painful in humans at concentrations equal or greater than 32.5%. In rats, it is unclear if unconsciousness following CO2 exposure occurs at concentrations below that associated with pain. A standardized loss of righting reflex (LORR) method was applied to identify CO2 concentrations associated with unconsciousness in rats. Additionally, a systematic review examined the consistency and completeness of LORR methods reported in the literature. The results from this thesis show that: 1) strain differences exist in the CO2 concentration required to induce LORR, 2) different LORR assessments can affect the results obtained, and 3) substantial inconsistencies in LORR methodology reporting exist in the literature. These findings raise awareness of strain-specific susceptibilities to CO2 exposure indicating that rat strains have different likelihoods of experiencing nociception and pain. In addition, this research identified longstanding persistent problems in the reporting of LORR methodology. In conclusion, the results from this thesis emphasize potential refinements to CO2 euthanasia and LORR methodological reporting.
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Open Access
A Genomic and Proteomic Survey of Traits that Modulate Antimicrobial Resistance in Staphylococcus aureus
(2024-04-18) MacKenzie, Colin Campbell; Lewis, Ian; Gregson, Daniel; Turner, Raymond
Antibiotic resistance is a growing global public health crisis which threatens to remove our primary treatment against bacterial infections. The mechanisms of antibiotic resistance in bacterial pathogens have been extensively studied, however questions surrounding the regulatory mechanisms of these resistance factors in clinical isolates are yet to be answered. In collaboration with the Broad Institute of MIT and Harvard, The Harvard T.H. Chan School of Public Health, and Alberta Precision Laboratories we completed whole-genome sequencing on 7,997 Staphylococcus aureus genomes from a larger study cohort of over 38,000 blood stream infections over a 16-year period. In addition to whole-genome sequencing, the proteomes of the bacterial isolates were quantitatively assessed using Tandem Mass Tag (TMT) ultra-high-performance liquid chromatography mass-spectrometry (UHPLC-MS) methods. Changes in protein levels and growth in the bacterial isolates are related to the variability in the genetic composition of the resistance operons of the specific clinical strains. This study has resulted in the understanding of a complex coregulatory interaction between two resistance operons of Methicillin Resistant S. aureus related to the mecA and blaZ resistance factors. Further, to better understand the metabolic adaptations of pathogens under antibiotic exposure, kinetic flux profiling of Escherichia coli metabolism under various antibiotic stressors was completed through the addition of fully labelled 13C-glucose. This intracellular flux monitoring via UHPLC-MS analysis, at a scale of seconds, has been used to gain insight into the metabolic alterations within E. coli metabolism under the exposure to twelve antibiotics spanning three common classes of antibiotics: DNA synthesis inhibitors, protein synthesis inhibitors, and cell wall synthesis inhibitors. This study has resulted in the classification of important metabolic adaptations occurring because of specific antibiotic compounds. Further, this intracellular metabolic study has shown evidence of a previously unexpected mevalonate pathway in E. coli. These studies have provided insight into the dynamics of pathogen interactions with antibiotics, and a deeper understanding of the antibiotic resistance mechanisms existing in pathogenic strains.
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Open Access
RGB Predicted Depth Simultaneous Localization and Mapping (SLAM) for Outdoor Environment
(2024-04-18) Brahmanage, Gayan Sampath; Leung, Henry; Wang, Yingxu; Hu, Yaoping; Bisheban, Mahdis; Gu, Jason
This thesis focuses on visual simultaneous localization and mapping (V-SLAM) for outdoor applications such as autonomous driving. While most V-SLAM methods have been tested on small-scale settings such as mobile robots, applying them in expansive outdoor spaces introduces additional complexities. The larger scale of the environment, dynamic obstacles, and depth-perception limitations of visual sensors pose challenges for V-SLAM methods. The first contribution introduces a dynamic V-SLAM approach. A novel front-end motion tracking approach is developed to recover multiple motions from image frames, considering key-points observed after map initialization as dynamic with time-varying locations. The proposed approach searches for key-point clusters based on their motion and classifies associated motions probabilistically. A bundle adjustment (BA) optimizes the local map, camera trajectory, and key-points motion within a unified V-SLAM system. BA maintains the geometric relationships between dynamic key-points and camera poses in the co-visibility graph, enhancing the overall robustness and accuracy of V-SLAM in populated environments. The second contribution of this thesis centers around a deep-learning-based depth prediction approach, which proves effective for estimating metric scale maps using a monocular camera. An unsupervised depth prediction approach is proposed using a novel convolution vision transformer (CViT) model architecture to infer depth from monocular images. The proposed encoder features a dual CViT block (DCViT); one block generates self-attention solely based on the spatial context of input feature vectors, and the other learns to generate attention based on the scene’s geometry. Contrastive learning of visual representations is applied to DCViT, where the model takes depth predictions from the same model through a feedback path as a supervisory signal to train the DCViT. Integration with residual blocks enables the learning of local and global receptive fields that produce predicted disparity maps at a higher level of detail and accuracy. Experimental results demonstrate significant improvements over state-of-the-art methods across multiple depth datasets. The third contribution of this thesis involves a comprehensive investigation into the utilization of predicted depth within monocular SLAM. This exploration aims to enhance the accuracy of map estimation in metric scale. Most existing approaches struggle with the non-Gaussian distribution inherent in heavy-tail noise produced by depth prediction models. The proposed monocular SLAM approach utilizes t-distribution for ego-motion, with parameter estimation achieved through maximum likelihood (ML) estimation using the expectation maximization (EM) algorithm. Experiments on real data show that the proposed t-distribution renders the monocular SLAM algorithm inherently robust to outliers and heavy-tail noise produced by depth prediction models.
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Open Access
Constructive alignment in a graduate-level project management course: an innovative framework using large language models
(2024-04-17) Pereira, Estacio; Nsair, Sumaya; Pereira, Leticia R.; Grant, Kimberley
Abstract
Constructive alignment is a learning design approach that emphasizes the direct alignment of the intended learning outcomes, instructional strategies, learning activities, and assessment methods to ensure students are engaged in a meaningful learning experience. This pedagogical approach provides clarity and coherence, aiding students in understanding the connection of their learning activities and assessments with the overall course objectives. This paper explores the use of constructive alignment principles in designing a graduate-level Introduction to Project Management course by leveraging Large Language Models (LLMs), specifically ChatGPT. We introduce an innovative framework that embodies an iterative process to define the course learning outcomes, learning activities and assessments, and lecture content. We show that the implemented framework in ChatGPT was adept at autonomously establishing the course's learning outcomes, delineating assessments with their respective weights, mapping learning outcomes to each assessment method, and formulating a plan for learning activities and the course's schedule. While the framework can significantly reduce the time instructors spend on initial course planning, the results demonstrate that ChatGPT often lacks the specificity and contextual awareness necessary for effective implementation in diverse classroom settings. Therefore, the role of the instructor remains crucial in customizing and finalizing the course structure. The implications of this research are vast, providing insights for educators and curriculum designers looking to infuse LLMs systems into course development without compromising effective pedagogical practices.