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The Role of Narcissism and Gender in the Career Success of North American Accounting Faculty
Get PDFThis thesis aims to investigate the impact of narcissism and gender on the career
success of accounting faculty in North America. Most of the current studies are focused
on investigating the impact of big-five personality traits and gender on the academic com-
munity in general and have produced mixed results. Little research attention has been
paid to examining the impact of negatively perceived personality traits such as narcissism
on faculty career success, and studies on the separate effects of grandiose and vulnerable
narcissism and their interactions with gender are even rarer. To bridge this knowledge
gap, a quantitative- and qualitative-based mixed method survey was designed to inves-
tigate the link between the two types of narcissism, gender, and academic career success.
Through Welch’s two sample t-tests and Stepwise multiple regressions, grandiose narcissism
was found to be significantly and negatively related to the number of PhD students super-
vised. In contrast to the significantly negative impact of vulnerable narcissism suggested by
existing studies, my results revealed that high vulnerable narcissism was significantly and
positively related to the amount of research grants obtained, the number of publications,
and the number of keynote speaker invitations. My research therefore provided evidence
about the negative side of grandiose narcissism and about the positive side of vulnerable
narcissism. Furthermore, in my sample, I observed that personality is more important than
gender in terms of career success, because female faculty had significantly higher salary,
greater number of publications and citations, and higher level of job satisfaction, even if
they are burdened by significantly heavier faculty service loads, have more household obli-
gations and caregiving duties; suffer more career interruptions; are more negatively affected
by the COVID crisis; and are more often specialized in non-mainstream research. Overall,
this research suggested complex relationships between personality traits, and gender, and
challenged prior findings. Future research may further investigate the impact of gender and narcissism in other contexts (i.e., other academic disciplines and countries, different faculty
age and ethnicity groups) and disentangle the gender effect from personality traits such as
narcissism through experimental research designs or more in-depth qualitative studies
Systematic Development and Validation of Predictive Models for the Removal of Indoor Gaseous Pollutants using Carbon-Based Filters
Get PDFAdsorbent media, which utilize physisorption and/or chemisorption to remove gaseous pollutants, are the most commonly employed technology for indoor air purification. The primary challenge associated with this technology is the saturation or exhaustion of the filter. Since conducting tests at low indoor concentrations (ppb level) is time-consuming and costly, it is necessary to develop models that can predict the service life of adsorbent filters based on experimental data obtained at high concentrations.
The main purpose of this research is to estimate the performance of activated carbon filters in removing a mixture of ozone and VOCs. Three VOCs with various properties, namely limonene, toluene, and methyl ethyl ketone, were selected. To achieve the final goal, models were developed progressively for the individual components (ozone or VOC) as well as for the VOC binary mixture. The unknown parameters of these models were determined using experimental data obtained from a bench-scale setup at ppm concentration levels. Subsequently, the models were validated at lower concentrations, a higher velocity, and on a full-scale setup. Pore gas-phase and surface diffusion were the dominant mass transfer steps for intraparticle mass transfer of zone and VOCs, respectively. On the other hand, axial dispersion was important in the interparticle mass transfer of all components. Furthermore, a first-order chemical reaction and a polynomial function effectively described the reactions involving fresh activated carbon and ozone, as well as the parallel deactivation of activated carbon through chemisorption and catalytic processes.
Using the information derived from modelling the removal of ozone, single VOCs, and binary mixtures of VOCs, the filter's performance was further modelled for the removal of binary and ternary mixtures of ozone and VOCs. The proposed model considers the generation of by-products resulting from the heterogeneous reaction between ozone and the reactive VOC (limonene) on the carbon surface. The rate constant for this heterogeneous reaction, formulated upon the Eley-Rideal mechanism, was determined by fitting the model to the experimental data. The obtained reaction constant was then used to validate the model's ability for binary and ternary mixtures of ozone and VOCs at typical indoor concentrations
Arab Muslims’ Explanatory Models of Mental Illness: A Mixed-Methods Inquiry Using Cultural Consensus Approach
Get PDFArab Muslims are one of the fastest growing populations in Canada. Similar to other ethnocultural minority groups, Arab Muslims report high rates of psychological distress compared to the general population. Despite these high rates, however, Arab Muslims tend to use mental health services less than the general population, entering treatment only when the symptoms have become severe and terminating treatment prematurely. This series of studies aimed to better understand the cultural models of mental illness and help-seeking used by Arab Muslims in Canada, focusing especially on aspects of these models that might contribute to social disparities in access to mental health care. To this end, three studies were conducted in the Montreal community following the sequential cultural consensus approach. In study 1, 54 Arab Muslims free listed (a) key Muslim values; (b) beliefs about mental illness; and (c) help-seeking strategies. Items listed by more than 10% of participants were used in the following study. In study 2, 40 Arab Muslims completed three pile-sorting tasks while speaking aloud as they sorted previously identified salient concepts. Qualitative and multidimensional scaling methods were used to characterize the piles. In study 3, 68 Arab Muslims completed a questionnaire designed based on results from the first two studies. Cultural consensus analysis was used to determine the degree to which participants agreed or disagreed with the identified cultural model. The extent to which endorsement of different aspects of the shared model influenced help-seeking preferences was also evaluated. Taken together, results confirmed that there is strong consensus around key Muslim values. However, individuals may emphasize some aspects of the model more than others. Additionally, results point to variations in beliefs about causes of mental illness, with some participants emphasizing biopsychosocial and some emphasizing spiritual causes of mental illness. Results showed that these variations have impact on help-seeking preferences. Overall, participants indicated higher willingness to use professional mental health services than anticipated, albeit with significant concerns about whether their religious ideas would be acceptable to these professionals. The usefulness of the mixed methods cultural consensus approach to cultural-clinical psychology questions is discussed
Experimental Investigation of the Flow Dynamics in Models of Patient-Specific Aneurysms
Get PDFThis work investigates the complex flow dynamics in patient-specific compliant models of Abdominal Aortic Aneurysms (AAA) using time-resolved Particle Image Velocimetry (PIV). Scans of multiple planes were performed on three different models: a healthy aorta, a 4-cm saccular AAA, and a 7-cm fusiform AAA. We discuss the differences in flow patterns in patient-specific models compared to idealized models from previous work. We note that the curvature of the aorta upstream from the aneurysm, specific placement of the iliac arteries, and the overall symmetry of the aneurysm have important effects on flow structures, such as increasing transient effects, vortex formation, and wall impingement. Viscous energy dissipation rate (VEDr) was also evaluated as it has been previously identified as a potentially good metric to assess the severity of some vascular diseases.
Finally, a modal analysis was performed on the velocity fields using Proper Orthogonal Decomposition (POD). The main modes obtained were inspected to identify the dominant structures, and the distribution of energy between the modes (Shannon entropy), and to create a reduced-order model of the flow. The results show that Shannon entropy was significantly different between the three models, suggesting that it can be a promising clinical parameter to evaluate the severity of AAAs
Verifying Sensor Readings and Event Notifications through Monitoring Co-located IoT Devices
Get PDFAs the number of smart environments is increasing, our reliance on IoT devices and sensors is also increasing. However, the data from sensors may not always be reliable as sensors can report incorrect sensor readings and event notifications due to sensor failure or a compromise by a malicious actor. Although there has been extensive research on sensor data verification, they have their own limitations. Most of them deal with only certain types of sensor data, either only verifying the events notifications or only the sensor readings. They use redundant sensors for verification which might incur additional cost and overhead. As those works are designed to learn from a single smart home data without collaborating with other smart homes, they cannot utilize more diverse data from multiple smart homes to achieve better accuracy.
In this thesis, we present an approach that learns the relationships among the co-located sensors and uses that relationship to verify the reported sensor readings, and detects event notifications (including masked and spoofed events) by monitoring co-located IoT devices and also learns collaboratively from the data of multiple smart homes without sharing the private data to a centralized server using federated learning. We implement our solution in the context of smart homes and evaluate its effectiveness using a public smart home dataset. For sensor reading verification, we achieve an R2 score of 0.98, and for event verification, we achieve an accuracy of up to 100% which is among the best of existing works
Numerical studies of thermal-mechanical responses of embankments under a changing climate in two first nation communities, Saskatchewan, Canada
Get PDFRecords show that ongoing global warming has changed the thermal condition of the ground in seasonally frozen areas of Canada, causing widespread ground surface settlement and harm to infrastructure, particularly embankments. In the current study, finite element numerical analysis is conducted to evaluate how climate change may influence the thermal-mechanical (TM) regimes in road embankments that are under climate conditions in two Indigenous communities in Saskatchewan, Canada, namely Yellow Quill and James Smith. This evaluation includes the analysis of embankment on the climate data from 1975 till 2100, where the data is divided into different time periods of Historical (1975-2000), Future-1 (2023-2048), Future-2 (2049-2074) and Future-3 (2075-2100. From each period, 5 representing years including extreme cold, extreme hot, expected hot, expected cold, expected mean years are considered to simulate the TM regimes with and without traffic loads. The relation for temperature-dependent thermal expansion coefficients of soils is derived and included in the modeling based on the ice content and a mixture theory. Temperature dependent mechanical properties are also involved to account for freeze-thaw induced stress redistribution and the related potential plastic deformation. According to the coupled thermal-mechanical study, which takes into account the Linear Drucker-Prager yield criterion for the stress analysis at critical location of embankments, cases with an extreme cold climate indicates the worst effect on the embankment foundation. It is reflected by the more significant temperature variations causing larger plastic zones in the toe of the embankment when compared with other climate scenarios. The extreme hot cases tend to generate more displacement on the road surface as the climate is getting warmer. The present study only sheds light on the thermal-mechanical aspect, and it does not include pore water flow behavior due to frost actions. Therefore, the result on the heave or settlement of embankment surface is not significant. Nevertheless, the inclusion of temperature-dependent thermal expansion coefficients considering the ice contents provides a better estimation of thermal-mechanical responses
Assessing the Efficacy of Test Selection, Prioritization, and Batching Strategies in the Presence of Flaky Tests and Parallel Execution at Scale
Get PDFEffective software testing is essential for successful software releases, and numerous test optimization techniques have been proposed to enhance this process. However, existing research primarily concentrates on small datasets, resulting in impractical solutions for large-scale projects. Flaky tests, which significantly affect test optimization results, are often overlooked, and unrealistic approaches are employed to identify them. Furthermore, there is limited research on the impact of parallelization on test optimization techniques, particularly batching, and a lack of comprehensive comparisons among different techniques, including batching, which is an effective but often
neglected approach.
To address research gaps, we analyzed the Chrome release process and collected a dataset of 276 million test results. In addition to evaluating established test optimization algorithms, we introduced
two new algorithms. We also examined the impact of parallelism by varying the number of machines used. Our assessment covered various metrics, including feedback time, failing test detection speed, test execution time, and machine utilization.
Our investigation reveals that a significant portion of failures in testing is attributed to flaky tests, resulting in an inflated performance of test prioritization algorithms. Additionally, we observed that test parallelization has a non-linear impact on feedback time, as delays accumulate throughout the entire test queue. When it comes to optimizing feedback time, batching algorithms with adaptive batch sizes prove to be more effective compared to those with constant batch sizes, achieving execution reductions of up to 91%. Furthermore, our findings indicate that the batching technique is on par with the test selection algorithm in terms of effectiveness, while maintaining the advantage of not missing any failures.
Practitioners are encouraged to adopt adaptive batching techniques to minimize the number of machines required for testing and reduce feedback time, while effectively managing flaky tests. Analyzing historical data is crucial for determining the threshold at which adding more machines has minimal impact on feedback time, enabling optimization of testing efficiency and resource utilization
Application of Reinforcement Learning for Condition-based Maintenance of Multi-Unit Systems
Get PDFMaintenance is a pivotal aspect of manufacturing systems, particularly those operating on a large scale. With the advent of data-driven methods and machine learning technologies, new avenues have opened for optimizing maintenance policies. In light of this, this thesis introduces advanced methodologies in Reinforcement Learning (RL) and Deep Reinforcement Learning (DRL) specifically tailored for large-scale parallel manufacturing systems. We conducted two major studies to advance the field: In the first study, an RL-based algorithm is proposed, moving beyond the traditional focus on system degradation levels to instead concentrate on the count of failed or unhealthy units. This shift allows for a more dynamic and nuanced approach to maintenance. Through Q-learning, our algorithm demonstrated significant superiority over conventional methods such as value iteration, particularly when applied to a system with four parallel units. In the second study, we delve into Deep Reinforcement Learning, developing a framework designed for multi-unit
systems experiencing stochastic degradation and unforeseen failures. Unlike traditional methods, our DRL approach incorporates a more intricate reward function that considers a wide array of factors ranging from production costs to maintenance crew deployment. Notably, this study was rigorously tested on a system comprised of 30 parallel units, making it particularly relevant for real-world, large-scale applications. Our research significantly broadens the applicability of machine learning methodologies in maintenance scheduling, demonstrating both robustness and adaptability. These contributions not only validate the efficacy of data-driven approaches in real-world settings but also lay the groundwork for future research in this crucial domain
A novel methodology assessing the use of Semi-Transparent Photovoltaics integrated onto Double Skin Facades and their impact on the energy consumption of buildings
Get PDFBuilding-integrated Photovoltaics (BIPV) can replace building elements in both facades and roofs improving at the same time the thermal, the electrical and the daylighting performance of the building. A novel approach in BIPV is the Double Skin Façade (DSF) that integrates Semi-Transparent Photovoltaics (STPV). In this approach, the air that passes within the cavity created between the layer of the STPV and the building, acts as a buffer zone and depending on the preferred strategy, it can be used for heating, cooling or ventilating the building. In addition, the STPV is the exterior layer of the envelope, controlling the solar gains but also allowing daylight into the interior space.
This thesis identifies the important parameters of a double skin façade integrating semi-transparent photovoltaics (DSF-STPV) as well as the gaps in the existing literature, namely the lack of experimental studies on mechanically-ventilated DSF-STPV buildings and the lack of investigation of heat transfer coefficients within the cavity in the presence of wind effects. In addition, it appears that there are neither tools to simulate such complex systems nor guidelines to assist architects and engineers to optimally design a DSF-STPV system.
A methodology was developed to assess the use of STPV integrated onto DSF and their impact on the energy consumption of buildings. The thermal model employed was verified in an outdoor experimental set-up of a mechanically-ventilated DSF-STPV and an insulating glazing unit (IGU) integrating STPV (IGU-STPV) built at Concordia University (Montreal, Canada). The forced convection within the cavity of the DSF-STPV has been investigated and three Nusselt number correlations were developed and validated. An experimental test-room was also used for a comparison between the DSF-STPV and the IGU-STPV under specific outdoor conditions. From the experimental analysis it has been found that a DSF-STPV can reduce the exterior heat losses due to wind, by more than 20%, whereas the total combined efficiency of the DSF-STPV, can reach the 75% level. The comparison between the DSF-STPV and the IGU-STPV presents increased electrical performance of the DSF-STPV up to 9% and lower average temperature difference that reaches up to 10oC.
The developed Nusselt number correlations were used for the development and validation of a parametric numerical model of a DSF-STPV. The model also allows the user to perform a parametric analysis changing the design parameters of the thermal zone and the DSF-STPV. This model can also simulate battery storage and its effect on peak demand. A parametric analysis was carried out for sixteen (16) different ASHRAE climate zones, two (2) insulation cases for every climate location (baseline and advanced), nine (9) different cavity widths, nine (9) different cavity velocity set-points and twelve (12) different strategies, changing the operation of the DSF-STPV.
An analysis of the optimal operation for all sixteen (16) ASHRAE climate zones was presented, concluding that the climate locations can be separated into three main categories based on their behaviour, i.e. hot and mild, cold, and extreme cold locations, as they present similar patterns and strategies to achieve minimal energy consumption. In addition, the parametric analysis has shown that, for most cases, practical cavity widths under or over 0.50 m - 0.60 m show similar behaviour.
The mismatch between the electricity production by the STPV and the electricity needed for heating and lighting by the adjacent building perimeter zones was investigated, for Montreal, Canada. With the use of a predictive heating strategy, the peak demand of the building can coincide with the peak of the electricity production, resulting in more than 80% reduction in the electricity consumption by the grid during the peak hours
Sexual Diversity and Institutional Change: Exploring the Process of Changing Education Systems in Alberta and Newfoundland/Labrador
Get PDFThis dissertation explores how Alberta and Newfoundland/Labrador gradually changed their education systems from laggards to leaders in the context of sexual diversity. I demonstrate how these changes occurred by integrating historical institutionalism and human geography. From historical institutionalism, I draw from theories of gradual institutional change to show how policy change in each province was a continuous process that occurred over a period of time. I draw from policy feedback theory to explain how these changes happened. More specifically, I use explaining-outcome process tracing to identify policy feedback mechanisms, which provide a detailed account of policy change within the political and historical context of each province, including the networks of social relations involved and the scales from which they emerged. From human geography, I draw from theories of place as relational to theorize provincial educational policy-making as a locality in which various networks of social relations from different scales converge and negotiate how to create safe school spaces for sexually diverse students. In this way, I illustrate how the feedback mechanisms and relevant networks of social relations interact and become institutionalized through provincial educational policy-making. Applying this framework to provincial education policy, I demonstrate how Alberta and Newfoundland/Labrador’s education system gradually changed through a policy feedback process. This framework makes two theoretical contributions. The first contribution is by identifying policy feedback mechanisms to make visible the process of gradual institutional change. The second contribution is by integrating policy feedback mechanisms with place/locality as relational to demonstrate how a multi-scalar right is localized as different networks of social relations converge to negotiate how to enact this right