Since the publication of Modigliani and Millers original theorem on capital structure, researchers have tried to find explanations to firms’ capital structure decision. The literature has been rather ambiguous, and the two main fields of thought is the predictions of the trade-off theory and the pecking order theory. Previous research finds empirical evidence supporting both theories, which is why the subject is an interesting field for further research. The literature on capital structure in a Swedish context is limited and could therefore benefit from further research in a time plagued by the adverse economic setting which is due to the covid pandemic. That is why this study’s aim were to examine the potential effects the pandemic had on firms’ capital structure decision. The study tests the effects on firms’ total leverage ratio as well as the effects on different debt maturities by including long- and short-term leverage ratios. Due to the characteristics of the pandemic the study aims to show the industry specific effects as well. We adopted a positivistic research paradigm when conducting this quantitative study with a deductive research approach. When collecting data, Refinitiv Eikon provided us with a population of 914 publicly traded Swedish firms. Due to data drop off the final sample that was used in the statistical analysis amounted to 450 firms, which were deemed to be sufficient to provide generalizable results for the population.  This study was unsuccessful in proving that the pandemic had any significant effect on public Swedish firms’ capital structure in any of the leverage ratios. We can however prove that the pandemic had a significant effect on individual industry’s capital structure. This thesis can also provide evidence that Swedish firms to some extent follow the pecking order theory. Our results suggest that firms deleverage as they grow, which is in line with said theory. On industry level we find proof for both the trade-off theory and pecking order theory, indicating that certain industries are affected differently depending on specific characteristics of their operations. We find evidence of both theories when assessing the profitability of firms. These ambiguous results could motivate further research on the subject in a Swedish context.

Computed tomography (CT) combined with deep learning (DL) has recently shown great potential in biomedical imaging. Complex DL models with varying architectures inspired by the human brain are improving imaging software and aiding diagnosis. However, the accuracy of these DL models heavily relies on the datasets used for training, which often contain low-quality CT images from low-dose CT (LDCT) scans. Moreover, in contrast to the neural architecture of the human brain, DL models today are dense and complex, resulting in a significant computational footprint. Therefore, in this work, we propose sparse optimizations to minimize the complexity of the DL models and leverage architecture-aware optimization to reduce the total training time of these DL models. To that end, we leverage a DL model called DenseNet and Deconvolution Network (DDNet). The model enhances LDCT chest images into high-quality (HQ) ones but requires many hours to train. To further improve the quality of final HQ images, we first modified DDNet's architecture with a more robust multi-level VGG (ML-VGG) loss function to achieve state-of-the-art CT image enhancement. However, improving the loss function results in increased computational cost. Hence, we introduce sparse optimizations to reduce the complexity of the improved DL model and then propose architecture-aware optimizations to efficiently utilize the underlying computing hardware to reduce the overall training time. Finally, we evaluate our techniques for performance and accuracy using state-of-the-art hardware resources. / Master of Science / Deep learning-based (DL) techniques that leverage computed tomography (CT) are becoming omnipresent in diagnosing diseases and abnormalities associated with different parts of the human body. However, their diagnostic accuracy is directly proportional to the quality of the CT images used in training the DL models, which is majorly governed by the radiation dose of the X-ray in the CT scanner. To improve the quality of low-dose CT (LDCT) images, DL-based techniques show promising improvements. However, these techniques require substantial computational resources and time to train the DL models. Therefore, in this work, we incorporate algorithmic techniques inspired by sparse neural architecture of the human brain to reduce the complexity of such DL models. To that end, we leverage a DL model called DenseNet and Deconvolution Network (DDNet) that enhances the quality of CT images generated by low X-ray dosage into high-quality CT images. However, due to its architecture, it takes hours to train DDNet on state-of-the-art hardware resources. Hence, in this work, we propose techniques that efficiently utilize the hardware resources and reduce the time required to train DDNet. We evaluate the efficacy of our techniques on modern supercomputers in terms of speed and accuracy.

Background: Insufficient resources and dynamic infection control policies during the COVID-19 pandemic created a resource-strained environment which necessitated frontline Health Care Providers (HCPs) to make ethical decisions frequently. Many of these ethical decisions included allocating scarce resources to optimally prioritize patients, resources, and clinician time. The transition from usual patient-centred care to care centred around infection control mandates and rationing resources forced HCPs to balance competing demands while trying to uphold high standards of care. This research aimed to understand the resource allocation decisions HCPs had to navigate during the pandemic and the ethical considerations guiding them. Methods: Using a qualitative case study approach, we aimed to document the type of ethical decision, reasoning used, and the action frontline HCPs took during the pandemic. Twenty-five semi-structured interviews were conducted with multi-disciplinary HCPs employed in a single community Intensive Care Unit (ICU) in Ontario. Resource allocation decisions were extracted from the transcripts and were analyzed using conventional content analysis. Results: Resource allocation decisions within critical care practice were ubiquitous and diverse. The constraints imposed by the pandemic and multiple provincial and organizational policies formed the context that necessitated these decisions. HCPs drew upon a range of ethical theories, notably Utilitarianism and Virtue Ethics, while prioritizing HCP safety and patient well-being. Resulting actions included prioritizing clinical tasks, establishing boundaries, and adapting practice patterns. Although these situations commonly evoked stress and frustration amongst HCPs, some positive internal responses were also described, including feelings of self-efficacy, resourcefulness, and team cohesion. Conclusion: In conclusion, analysis of resource allocation-derived decision-making illuminated a variety of challenges that HCPs faced during the COVID-19 pandemic, driven by institutional policies and pragmatic limitations. Insights from this study underscore how these ethical decisions are an inherent part of clinical practice and have the potential to foster positive professional development amidst adversity. / Thesis / Master of Health Sciences (MSc) / During the COVID-19 pandemic, Health Care Providers (HCPs) faced difficult choices due to limited resources and evolving infection control rules. HCPs had to decide how to allocate scarce resources and balance competing demands while maintaining high standards of care. This research aimed to understand the types of resource allocation decisions HCPs made and the ethical considerations guiding them. Through twenty-five interviews with HCPs from an Intensive Care Unit in Ontario, we found that such decisions were both frequent and diverse. HCPs considered ethical theories like Utilitarianism and Virtue Ethics, prioritizing patient well-being and HCP safety. They often had to adapt their practices, establish boundaries, and determine priorities. Although these decisions caused stress and frustration, they also fostered team cohesion and personal growth. Overall, this research sheds light on some challenges HCPs faced during the COVID-19 pandemic and underscores the importance of preparing learners to navigate ethical decision-making in clinical practice.

Objectif : La Covid-19 a conduit à de nombreuses complications. Parmi celles-ci, on compte le syndrome post-Covid-19, qui inclut fréquemment le malaise post-effort (MPE), observé dans environ 90% des cas. Après une analyse du profil sociodémographique des personnes atteintes de MPE, le présent mémoire a visé à comprendre ce phénomène en évaluant les différentes variables du MPE en fonction du niveau d'activité physique (AP) des personnes touchées. Méthodes : La collecte des données a été faite à l'aide d'un questionnaire mis en ligne sur Lime Survey et distribué via Facebook. Le questionnaire visait à évaluer le niveau d'AP (loisirs, travail et transport) avant et après l'infection par la Covid-19. La population ciblée pour cette étude était constituée de personnes ayant présenté le syndrome post-Covid-19, soit le MPE. Résultats : Globalement, les résultats indiquent que les individus avec MPE (n=154) sont moins actifs et plus sédentaires après avoir contracté la Covid-19 qu’avant. Toutefois, les analyses d’interactions ont permis de faire ressortir des éléments importants. Les personnes atteintes de MPE ont vu un maintien ou une diminution significative de l'AP au travail et totale (intensité moyenne et élevée) après avoir contracté la Covid-19. De façon plus spécifique, ce sont particulièrement les femmes et les personnes dont la dernière infection remonte à plus d'un an qui ont été touchées par cette baisse. Alors que des déplacements à pieds ont diminué chez les femmes, ils ont augmentés chez les hommes. Par ailleurs, les déplacements à vélo ont diminués après la Covid-19 et ce, plus particulièrement pour les hommes, indiquant un potentiel déplacements du vélo vers la marche pour le transport. Répondre positivement aux 5 questions sur le MPE était aussi associé à une plus grande réduction de l’AP d’intensité moyenne ainsi que moyenne et élevée au travail mais à une augmentation moins marquée de la sédentarité. Avoir une récupération plus lente était associée à une réduction plus importante de l’AP d’intensité moyenne à élevée au travail. Et alors que les individus non effrayés par la peur de faire un malaise post-effort augmentaient leur niveau d’AP (intensité moyenne et moyenne/élevée) au travail, ceux effrayés le réduisait à la suite de la Covid. Finalement, ni l’âge, ni le nombre de contaminations qu’ils ont subi et ni le lieu de naissance des répondants avec le MPE n’ont pas eu d’effet sur les changements du mode de vie. Conclusions : Cette étude révèle que les personnes atteintes du syndrome post-Covid-19 à risque de MPE sont non seulement plus sédentaires mais qu’elles rapportent une baisse de l'AP qui touche surtout le travail et ce, de façon plus prononcée chez les personnes gravement atteintes de MPE et chez les femmes. Ces résultats sont importants à considérer dans un contexte de la reprise d'AP au travail post-Covid-19 de ces patients qui peuvent avoir des niveaux de déconditionnement physique plus importants. / Objective: Covid-19 has led to numerous complications. Among these, we count the post-Covid-19 syndrome, which frequently includes post-exertional malaise (PEM), i.e., in about 90% of cases. After analyzing the sociodemographic profile of people with PEM, the current thesis aims to understand this phenomenon by evaluating the different variables of PEM according to the level of physical activity (PA). Methods: We collected data using an online questionnaire hosted on Lime Survey and distributed via Facebook. The questionnaire aimed to assess the level of PA (leisure, work, and transport) before and after Covid-19 infection. The target population for this study consisted of individuals who exhibited post-Covid-19 syndrome, also known as PEM. Results: Overall, the results indicate that individuals with PEM (n=154) are less active and more sedentary after contracting Covid-19 than before. However, interaction analyses have brought out some important elements. People suffering from MPE experienced either a maintenance or a significant decrease in PA at work and overall (medium and high intensity) after contracting Covid-19. More specifically, it was particularly women and people whose last infection was more than a year ago who were affected by this decrease. While walking decreased among women, it increased among men. Moreover, cycling decreased after Covid-19, especially among men, indicating a potential shift from cycling to walking for transportation. Responding positively to the 5 PEM questions was also associated with a greater reduction in medium-intensity PA and medium to high-intensity work, but with a less pronounced increase in sedentary behavior. Having a slower recovery was associated with a greater reduction in medium to high intensity PA at work. And while individuals not scared of PEM increased their level of PA (medium intensity and medium/high) at work, those scared reduced it following Covid. Finally, neither age, nor the number of infections they suffered, nor the birthplace of respondents with ME/CFS had an effect on lifestyle changes. Conclusions: This study reveals that individuals with post-Covid-19 syndrome at risk of PEM are not only more sedentary, but they also report a decrease in PA, primarily impacting work, and this is especially pronounced in those severely affected by PEM and in women. These findings are important to consider in the context of resuming PA at work post-Covid-19 for these patients who may have higher levels of physical deconditioning.

This dissertation addresses the lived experiences of Florida college students during the COVID-19 pandemic, specifically the communication that was provided to them through their university on the subject of the pandemic. This two-stage research process first involved the collection of survey responses on a variety of pandemic communication-centered topics from 39 university students from three universities (University of Central Florida, University of Florida, and University of South Florida). Second, a subsequent interview with 7 students provided qualitative information to further explicate themes and trends seen through data analysis. Using health and technical communication alongside crisis communication analyses to understand what elements of university information transmission were most effective and recollected, the triangulation of data points and recommendations for future pandemic communication were able to be created in an effort to provide a better framework for student expectations in the future pandemics. Results of students’ opinions on their university’s communication were that overall messaging was strong and confidence in their institution was generally high, with some negative sentiments on how universities handled the struggle of clubs and universities and onboarding new students into the academic environment during the pandemic. Recommendations were made for universities to prepare frameworks for university students to maintain their abilities to organize and join clubs during significant disruptions to campus life, to retain and build on the confidence students had in their position as an authority on health information, and to resume pandemic messaging due to the ongoing risks to health from long COVID and reinfection.

This study investigated school closures' impact on students from vulnerable populations in elementary classrooms. The research places an emphasis on the importance of an equitable and effective educational system in every community. It highlights the role of formal education in providing opportunities for social advancement and promoting the development of individuals and nations. The impact of the COVID-19 Emergency on access to quality education and the challenges and inequities faced by vulnerable populations are emphasized. This research also discusses the Sustainable Development Goals (SDGs) established by the United Nations in 2015 to ensure inclusive and equitable education for all children worldwide. Data from a large suburban school district is presented to analyze how students perform following school closures and their attempts to prevent achievement gaps. Specifically, the researcher examines how students from minority and low socioeconomic subgroups have been impacted by the COVID-19 Emergency compared to their peers. The analysis demonstrates a significant correlation between race, socioeconomic status and student success on the Florida Standards Assessment. Due to school shutdowns, one can conclude that students from vulnerable populations struggled to access curriculum at the same rate as their non-vulnerable peers. Recommendations are made for mitigating learning loss and for preparing schools to successfully maintain the quality of education in future emergencies.

Viral infection stimulates the immune response to produce many cytokines and chemokines, the proteins imperative to fight a brewing infection. This response begins through recognition of pathogen-associated molecular patterns (PAMPs) from the virus, or from other signatures characteristic of tissue damage, damage-associated molecular patterns (DAMPs), by pattern recognition receptors (PRRs) that in turn stimulate pro-inflammatory signaling cascades. The results of these signaling pathways include the release of cytokines and chemokines that work to further upregulate immune responses and attract immune cells to the site of infection, respectively. In the case of SARS-CoV-2 infection, these responses can become problematic if they go unmitigated or unresolved, resulting in the severe COVID-19 manifestation of the 'cytokine storm,' or multisystem inflammatory syndrome in children (MIS-C). One classically increased protein in cytokine storm of COVID-19 patients is C-X-C motif chemokine 10 (CXCL10), which has been explored as a prognostic marker as it is shown to be predictive of disease outcome in hospitalized patients. To prevent severe outcomes like cytokine storm, a delicate balance must be struck, to ensure that this inflammation does not result in high levels of diffuse tissue damage. To achieve this, anti-inflammatory pathways exist within the immune system and help dampen the signals being induced. One such unique anti-inflammatory protein is a pattern recognition receptor known as NLRX1 (Nucleotide binding oligomerization domain, leucine rich repeat containing X1), that can interact with two main pathways involved with anti-viral immunity, the NFB and interferon pathways, downregulating them to keep off-target tissue damage at bay. NLRX1 is also involved in several other cellular processes, including modulating cell death processes and cellular metabolism which can also impact viral replication and clearance indirectly. In this work, we investigated both the pro- and anti-inflammatory arms of the anti-SARS-CoV-2 response focusing on two key proteins – pro-inflammatory chemokine CXCL10 and immunoregulatory PRR NLRX1. The roles of these two proteins were explored utilizing transcriptomic analysis of both human and mouse RNA samples, immortalized cell culture work, humanized mouse models of SARS-CoV-2 infection, and mouse-adapted virus models to be able to utilize deficient mouse models. In this work we better characterize the immune response to SARS-CoV-2 and its related immune-driven pathobiology of disease. The data presented in this work continues to elucidate CXCL10's role as an important driver of viral clearance of SARS-CoV-2, translating data from human patient nasal swabs to the animal model of disease, exploring differential inflammation and immune responses in the absence of CXCL10. Additionally, the work shown here provides further understanding of NLRX1 and its role in antiviral immunity with the context of SARS-CoV-2 infection. The interactions between this protein and the virus remains to be fully characterized, however, it appears they have some degree of mutual inhibition as determined by animal and cell culture models. The culmination of work here emphasizes the importance for both the pro- and anti-inflammatory responses in SARS-CoV-2 infection and offers insight into two possible related targets for future drug development. / Doctor of Philosophy / When a virus invades the body, the immune system kicks off many signaling cascades to keep the virus from replicating, clear virus already established in cells, and clean up the tissues surrounding the infected area of the remnants of cells that already succumbed to the virus. While this immune response is important to fight off the virus that has made its way into the body, overactive immune responses can result in hospital stays requiring supportive care to aid recovery from possible off-target tissue damage. One such case of this happening is when SARS-CoV-2 induces such a strong response, the immune system becomes overzealous and results in overproduction of pro-inflammatory cytokines and chemokines, signaling proteins in the immune system, which can lead to the characteristic 'cytokine storm' of severe COVID-19 disease. One of the proteins most often overproduced is the chemokine CXCL10, and this protein has been used as a biomarker in clinical practice to successfully predict severe disease outcomes in COVID-19 patients. To help combat severe disease outcomes and high levels of tissue damage, the immune system has inborn checks and balances to ensure that proteins like CXCL10 do not reach the level of overproduction as in the cytokine storm of COVID-19. One of these natural checkpoints is a protein called NLRX1, which interacts with two of the main pathways that can lead to the overproduction of cytokines seen overproduced in the case of cytokine storm. NLRX1 also has other roles in other interesting facets important for viral infections, including the metabolism of the cell and cellular death processes. The culmination of these roles could offer up NLRX1 as a possible target for treatments in the future. The work put together here explores both sides of the immune response, turning it 'on' with pro-inflammatory signaling, and turning it 'off' with anti-inflammatory signaling, trying to find just the right amount of inflammation to clear a viral invader while also impeding off target and diffuse tissue damage as the body fights the virus. This work focuses on two key proteins, CXCL10 to represent pro-inflammatory responses, and NLRX1 to represent the anti-inflammatory signaling. Understanding both arms of the immune response to SARS-CoV-2 infection is crucial to being able to identify potential targets for future treatments to help combat severe outcomes of SARS-CoV-2 infection. Using multiple levels across the translational spectrum, including cell culture, animal models, and human patient RNA from COVID test swabs, we explore both facets of SARS-CoV-2 immunity, focusing on these two proteins. Utilizing mouse models bearing deletions of the genes required to make these proteins and a mouse-adapted strain of SARS-CoV-2, this work characterizes how important these individual proteins are in the immune response to SARS-CoV-2, and work as proxies to understand the broader impacts of either the positive or negative regulation of immune signaling. Because of the work culminated here, these two tangentially related proteins are also offered up as possible future drug targets for the development of treatments in severe COVID-19 disease with cytokine storm presentation.

Yes / Since December 2019, a new type of coronavirus called novel coronavirus (2019-nCoV, or COVID-19) was identified in Wuhan, China and on March 11, 2020, the World Health Organization (WHO) has declared the novel coronavirus (COVID-19) outbreak a global pandemic. With more than 101,797,158 confirmed cases, resulting in 3,451,354 deaths as of May 21, 2021, the world faces an unprecedented economic, social, and health impact. The clinical spectrum of COVID-19 has a wide range of manifestations, ranging from an asymptomatic state or mild respiratory symptoms to severe viral pneumonia and acute respiratory distress syndrome. Several diagnostic methods are currently available for detecting the coronavirus in clinical, research, and public health laboratories. Some tests detect the infection directly by detecting the viral RNA using real time reverse transcriptase polymerase chain reaction (RT-PCR) and other tests detect the infection indirectly by detecting the host antibodies. Additional techniques are using medical imaging diagnostic tools such as X-ray and computed tomography (CT). Various approaches have been employed in the development of COVID-19 therapies. Some of these approaches use drug repurposing (eg Remdesivir and Dexamethasone) and combinational therapy (eg Lopinavir/Ritonavir), whilst others aim to develop anti-viral vaccines (eg mRNA and antibody). Additionally, health experts integrate data sharing, provide with guidelines and advice to minimize the effects of the pandemic. These guidelines include wearing masks, avoiding direct contact with infectious people, respiratory and personal hygiene.

Yes / Planetary health sees neoliberal capitalism as a key mediator of socioecological crises, a position that is echoed in much COVID-19 commentary. In this Personal View, I set out an economic theory that emphasises some of the ways in which neoliberal capitalism's conceptualisation of value has mediated responses to COVID-19. Using the intersection of ecological, feminist, and Marxist economics, I develop an analysis of neoliberal capitalism as a specific historical form of the economy. I identify the accumulation of exchange value as a central tendency of neoliberal capitalism and argue that this tendency creates barriers to the production of other forms of value. I then analyse the implications of this tendency in the context of responses to COVID-19. I argue that resources and labour flow to the production of exchange value, at the expense of production of other value forms. Consequently, the global capitalist economy has unprecedented productive capacity but uses little of this capacity to create the conditions that improve and maintain people's health. To be more resilient to coming crises, academics, policy makers, and activists should do theoretical work that enables global economies to recognise multiple forms of value and political work that embeds these theories in societal institutions.

Yes / Contact tracing involves collecting people’s information to track the spread of COVID-19 and to warn people who have been in the proximity of infected individuals. This measure is important to public health and safety during the pandemic. However, customers’ concerns about the violation of their privacy might inhibit their cooperation in the contact tracing process, which poses a risk to public safety. This research investigates how to facilitate customers’ cooperative behavior in contact tracing based on cognitive trust and affective trust. The findings show that cognitive trust increases people’s willingness to disclose information and reduces their willingness to falsify it, whereas affective trust increases the willingness for both disclosure and falsification. This research contributes to the literature on customer data privacy by illuminating how cognitive and affective trust distinctly influence cooperative behavior, which has important implications for hospitality businesses.