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Career Experiences of Non-Profit Organization (NPO) Employees: An Exploration Research of NPO Employees’ Work Meaningfulness and Work-life Balance.
Get PDFWith the recent challenges societies have had to face, including natural disasters and wars, non-profit organizations and the aid they provide, by the means of a mixed workforce: both local and expatriates, have become even more prevalent. However, they are facing a “talent war” as they are struggling to retain and attract new employees. Mega events, including COVID-19, have shed additional light on certain elements related to one’s well-being, mainly one’s work-life balance and work-meaningfulness, making them more valuable to employees, thus important to organizations wanting to limit turnover rates. These working experiences are seemingly still lacking understanding across the non-profit industry due to scarce research on the topic. This micro-levelled qualitative study will further explore the working experiences of non-profit employees, locals and expatriates, across a variety of non-profit organizations. 21 participants were recruited across 5 countries and their interviews were thematically analysed. Findings show that non-profit employees have an overall good sense of work meaningfulness. Work-life balance was found satisfactory by interviewees’ self-perceptions but was objectively more conflicting. Expatriates were found to have an overwhelming greater sense of work-meaningfulness compared to locally employed workforce which was seemingly associated with greater work-family conflicts. This research contributes not only to the current non-profit literature but also to practitioners, by bringing additional insights into the working experiences held across the non-profit industry as well as those that employees enjoy experiencing. Future research should focus on further establishing quantitatively this research’s findings
Socially Aware Path Planning For Autonomous Road Vehicles
Get PDFThis research addresses the critical challenge of path planning for autonomous vehicles by integrating Social Value Orientation (SVO) into path-planning algorithms for autonomous vehicles. The framework utilizes a fuzzy logic-based system to evaluate and categorize the social values of pedestrians and vehicles in real time by considering their observed behaviors and social cues. This approach enables autonomous vehicles to make more informed and socially aware decisions, thereby enhancing their ability to interact safely with human road users. In addition, the thesis introduces an adaptive artificial potential field (APF) method that dynamically assesses the dangers presented by different road users, taking into account factors such as type, size, speed, and societal importance. By integrating road layout and traffic signal potential fields, the APF method guarantees that the autonomous vehicle can navigate intricate road conditions while prioritizing safety. The effectiveness of the proposed path planning framework is rigorously validated using CARLA driving simulator. These simulations create a realistic and dynamic traffic environment, allowing for the thorough testing of custom behavioral profiles for various actors in different traffic situations. Results demonstrate the framework's practical applicability and effectiveness in enhancing the interaction between autonomous vehicles and human road users.
The outcomes of this research contribute to the development of safe and human-centric path-planning algorithms for highly automated vehicles, particularly in dense or mixed-traffic environments. This work represents a significant step towards creating autonomous systems that can coexist harmoniously with human drivers and pedestrians, ultimately leading to safer and more efficient roadways
Investigating freshwater bacterial diversity, community composition and function in hundreds of Canadian Lakes
Get PDFFreshwater bacterial communities play important roles in global biogeochemical cycling and aquatic food webs, yet bacterial diversity, community composition, and community metabolism in freshwater ecosystems remain less explored compared to terrestrial and marine
ecosystems. In this thesis work, I investigated bacterial communities in hundreds of lakes located across Canada, a country that contains millions of lakes. Utilizing 16S rRNA and metagenomic techniques, this research explores diversity patterns, community composition and functional
capabilities of lake bacterial communities and links variation in these three components to human-mediated alterations, specifically watershed land use types within lake watersheds.
In the first research chapter, I performed an investigation of communities in 403 lakes from seven ecozones. I identified distinct bacterial diversity patterns between western (Semi-Arid Plateaux, Prairies, and
Boreal Plains ecozones) and eastern (Boreal Shield, Mixedwood Plains, Atlantic Maritimes, and Atlantic Highlands ecozones) Canada. The identified pattern was primarily influenced by lake physicochemistry including productivity, ion concentration, and lake depth. Bacterial community structure was influenced particularly by lake pH and trophic state.
In the next research chapter, I expanded the study to 621 lakes across 12 ecozones and explored variation in diversity and community composition patterns in relation to water quality and land use. Total phosphorus (TP)
was identified as a key variable shaping community composition, with notable shifts occurring at 110 μg/L TP. Variation in bacterial communities within the Prairies ecozone were driven by agriculture while urbanisation played a role in structuring community composition within the
Pacific Maritimes ecozone.
In the final research chapter, I investigated bacterial functional capabilities using gene-centric metagenomics. Physicochemical parameters emerged as top predictors of variation in functional gene composition, with xenobiotics biodegradation and metabolism notably influenced. Overall, the research presented in this thesis demonstrates that bacterial diversity, community composition, and community function exhibit variations across continental and regional scales that can be attributed to within-lake conditions and watershed land use types
Transmission of Shocks on Real Assets of Corporations to Their Financial Performance
Get PDFThis thesis investigates how shocks to real assets impact the financial performance of corporations.
The first study examines drought shocks and their effect on bank financial stability and loan performance. Unlike other climate shocks, droughts are slow and their effects are not immediate. By defining a two-year drought shock at the bank level and using a difference-in-differences methodology, it is found that drought shocks significantly worsen banks' Z-Score, return on assets, and stock volatility. Additionally, non-performing loans increase substantially in affected banks compared to unaffected ones. The economic impacts of droughts are comparable to a 1% decline in the unemployment rate. Affected banks tend to close branches in drought-impacted regions without increasing their capital ratios.
The second study investigates the impact of human mobility reductions during the COVID-19 pandemic on bank performance. Using geographic locations of bank branches to measure exposure to mobility declines, the study finds that U.S. banks' stability, profitability, and asset quality are negatively affected by reduced mobility. There is a significant rise in non-performing consumer loans, residential, and commercial mortgages, while commercial loans remain unaffected. Relationship banking provides a protective buffer against the adverse effects of mobility reductions on banks.
The third study assesses the role of unpledged collateral in Real Estate Investment Trusts (REITs) concerning liquidity constraints and asset sales. The findings indicate that REITs with higher ratios of encumbered assets tend to sell properties at lower prices and face increased loan spreads. Encumbrance restricts the pool of assets available for disposal, reducing profits from selling activities. However, the negative impact of encumbrance on selling activity and loan spreads is mitigated for financially healthy REITs. Overall, the study underscores the importance of unencumbered assets as a determinant of selling activity and borrowing costs.
Collectively, these studies provide valuable insights into how different types of shocks affect financial stability, loan performance, and asset sales, highlighting the critical role of asset management and strategic responses in mitigating adverse outcomes
A Comparison of Students’ Models of Knowledge to be Learned in an Introductory Linear Algebra Course with Results from Prior Research on Such Models in College Calculus Courses
Get PDFResearch done from an institutional perspective has found students to develop non-mathematical practices in college calculus courses that emphasize routinization of knowledge. The knowledge students are expected to learn, as indicated by tasks determining their grade in the course, enables students to routinize techniques and use non-mathematical considerations, such as didactic and social norms from their course, to justify their techniques. Such research has mostly been done in the calculus context. To calibrate the study of the effects of institutionalized routinization of knowledge, I investigated these in the context of a course in a different domain of mathematics and regulated by institutional mechanisms similar to those regulating college calculus courses. To this end, I adapted, to an introductory college linear algebra course at a large urban North American university, the framework and methodology from a body of research that qualifies students’ activity by attending to institutional mechanisms that regulate it. The framework appends to the Anthropological Theory of the Didactic (ATD) (Chevallard, 1985, 1999) notions from the Institutional Analysis and Development framework (IAD) (Ostrom, 2005). The ATD provides tools through which to model activities that occur in institutions and the IAD elaborates institutional mechanisms that regulate activity that occurs in institutions. I analyzed curricular documents to develop task-based interviews (TBI) that could draw out the nature of the knowledge students mobilize. I conducted interviews with ten students shortly after they had completed the course. The qualitative approach I used included an analysis of curricular documents to model knowledge to be learned in the course that relates to each TBI task, as well as an analysis to model the knowledge students mobilized in response to each TBI task. I found students mobilized non-mathematical practices: what they activated was conditioned by and delimited to knowledge normally expected of students in the course, and their mobilization contrasted in various ways with mathematics intrinsic to the tasks they were offered. I also propose an operationalization of the institutional notion of positioning previously proposed and examined as a mechanism regulating students’ activity in didactic institutions
On Critical Controllers: Exploring Reflective Game Design Through Altctrl Devices, Games, and Practices
Get PDFAlternative game controllers, interfaces, and controls (altctrl) and the games designed around and through them provide novel arrangements of players and games, significantly altering play experiences. Altctrl works can take a variety of forms, including, but not limited to, custom devices coupled with new software and repurposed artifacts or play practices. They have gained cultural momentum through game jams, showcases, and specialized events. While often valued for innovation and entertainment, altctrl practices can also challenge hegemonic conventions, fostering critical, oppositional, and reflective approaches to game making and play. I set out to examine how altctrl articulates alterity and the unconventional, addressing issues of openness, materiality, criticality, and reflection. This effort is in conversation with the fields of critical and reflective game design, game design research, and game production studies. It also aims to benefit altctrl practitioners, students and educators, and game creators.
This research combines mutually informed research-creation and qualitative research approaches. I elaborate a model for understanding altctrl as devices, games and practices, drawing from analysis of primary and secondary interviews with practitioners, online documentation of events and other works, and previous scholarship. I developed research-creation projects focused on criticality and reflectivity: an altctrl game discussing political systems and game interfacing; altctrl devices questioning do-it-yourself and circulation practices; and a set of phone-based altctrl games and tools exploring possible infrastructures for altctrl practice. These projects’ development was carefully documented, allowing for analysis focused on their reflective and critical potentials. The insights from research-creation informed the construction of an expanded set of design qualities to support reflective game design
Conditional Cooperation in Public Goods Games and Perception of Corruption: A Comparative Study between Nigeria and Canada
Get PDFConditional cooperation is the tendency to cooperate if and only if others cooperate. This paper aims to ascertain the importance of conditional cooperation and the effect of perceived corruption and cultural environments on individuals’ behaviors in the public goods games in a public game by replicating the seminal Fischbacher et al. (2001) experiment and comparing results between Nigeria and Canada. This thesis proposes to explore the reliability of classifying participants into distinct behavioral types – such as conditional cooperators, free riders, and triangular contributors – based on their contribution patterns. Additionally, a post-experiment questionnaire – which is modified based on the European Social Survey (ESS), Value Survey Model (VSM, 2013), and the United Nations Office on Drugs and Crime (UNODC) and National Bureau of Statistics (NBS) National Survey on Quality and Integrity of Public Services – is employed to explore how individuals in both countries perceive their cultural and socio-economic differences, and how perceptions, particularly regarding corruption, influence their cooperative behavior and contributions to public goods
Development of An Integrated AI-Based Online System for Lake Chlorophyll-a Concentration Modeling and Monitoring (CMMOS)
Get PDFThis thesis presents the development of the Chlorophyll-a Modeling and Monitoring Online System (CMMOS), an innovative Artificial Intelligence (AI)-based tool designed to enhance the monitoring and prediction of chlorophyll-a concentrations in lake ecosystems. Traditional methods of monitoring these concentrations face limitations in real-time data processing and the handling of complex environmental interactions. CMMOS addresses these challenges by integrating a sophisticated array of machine learning models, including Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), Gradient Boosting Tree (GBDT), Multi-Layer Perceptron (MLP), Long Short Term Memory (LSTM), K Nearest Neighbors (KNN), Multiple Linear Regression (MLR), and Extreme Gradient Boosting (XGBoost). The system's efficacy was rigorously evaluated using comprehensive datasets from Lake Champlain and Lake Simcoe. Through these evaluations, the system demonstrated high predictive performance, particularly for models like RF, GBDT, and XGBoost, which excelled across various metrics. Data preprocessing techniques including Missing Value Imputation, Outlier Detection, and Feature Selection proved critical in enhancing the accuracy and reliability of these models. CMMOS contributes to the field of environmental science by offering a real-time, data-driven approach to lake water quality management. The system facilitates dynamic monitoring and predictive analysis, enabling stakeholders to make informed decisions promptly. It illustrates the substantial advantages of utilizing AI in ecological monitoring and management. Recommendations for future work include further optimization of machine learning models, exploration of ensemble techniques to refine predictive accuracy, expansion of the system to include more diverse environmental variables, and enhancements to the user interface to better serve various users. This thesis lays a robust foundation for future advancements in AI applications for environmental monitoring, aiming to improve the sustainability and effectiveness of lake management practices
Advanced Decoration of Cost-Effective Transitional Metals for Photocatalytic Hydrogen Production
Get PDFThroughout history, the energy crisis has predominantly arisen from inadequate supply rather than depletion of resources. From the utilization of fire to the development of steam engines and, subsequently, the utilization of fossil fuels and renewable energy sources, humanity has consistently adapted to meet its energy needs. Particularly noteworthy, hydrogen stands as a significant and renewable energy source. However, the demand for hydrogen has tripled since 1975, and due to technological limitations and high costs, most of its production still depends on fossil fuels. Various approaches have been employed to address this issue, one of which is photocatalysis, which holds promise in mitigating the adverse effects of fossil fuel production. Nevertheless, this method has limitations regarding light absorption and the recombination of charged particles. Zero and one-dimensional nanostructures, such as nanotubes, have been extensively investigated as potential photocatalysts, as they exhibit unique properties, including a high aspect ratio that facilitates the controlled movement of charges in a single direction. In order to overcome the aforementioned limitations, modifications have been made to these nanostructures.
However, conventional methods employed for these modifications have drawbacks, such as the extensive use of toxic solvents, limited control over the process, time constraints, and lack of reproducibility. In contrast, greener synthesis methods, such as microwave deposition and liquid-assisted resonant acoustic mixing (LA-RAM), have emerged as viable alternatives due to their minimal energy input and reduced reliance on toxic solvents. Moreover, microwave deposition and liquid-assisted resonant acoustic mixing offer advantages such as shorter reaction times, controlled and uniform deposition, scalability, and improved reproducibility, which address the challenges faced by conventional methods. In this study, transitional metals, namely copper and nickel, were decorated onto titania nanoparticles using LA-RAM and titanium nanotubes using microwave deposition. The individual and combined effects of copper and nickel decoration were investigated alongside optimizing some reaction parameters. Compared to conventional noble metal decoration and traditional synthesis, the photocatalytic efficiency of the resulting best outcome proved to be a practical and effective option. This research lays a foundation for further exploration and utilization of greener and more cost-effective methods and metals in synthesizing photocatalysts
Resource efficient deep learning approaches for monaural speech separation
Get PDFSpeech separation is a critical task in processing naturalistic audio streams, aiming to extract individual speech sources from mixed speech signals. Monaural speech separation, which deals with audio from a single microphone, focuses on isolating overlapping speech signals, a process essential for applications such as automatic speech recognition and voice assistant devices. Recent advances in deep learning have significantly improved speech separation, typically by training neural networks to estimate high-quality separated speech from mixed signals using supervised
learning. However, most state-of-the-art neural networks operate in the time domain and are computationally expensive due to their sequential processing methods and complex structures. Despite the common perception that time-domain models outperform those in the time-frequency domain, this thesis focuses on developing resource-efficient models in the time-frequency domain, aiming to enhance their performance within a deep learning framework.
In the first contribution of this thesis, we propose RCFormer, a Conformer-based neural network with a redundancy approach, designed for monaural two-speaker speech separation. The RCFormer employs multiple pairs of intra-frame and sub-band Conformer blocks to successively capture both frame-level and sub-band-level information from the input spectrogram. To address the challenge of sparse information in the input spectrogram, a redundancy approach is introduced to create a denser representation by stacking the input spectrogram embeddings. The proposed architecture integrates Conformer blocks between a dilated dense convolutional encoder and decoder, with the Conformer block outputs fed into a masking module that generates masks to filter the encoder outputs, which are then transformed into separated speech signals via the decoder. Extensive experiments demonstrate that RCFormer achieves competitive, and often superior, performance compared to existing state-of-the-art methods across all evaluation metrics, while also featuring significantly fewer trainable parameters.
While many models achieve competitive performance with fewer trainable parameters, few researchers have addressed the computational workload and processing time associated with these models. In the second contribution of this thesis, we propose FSBNet for two-speaker speech separation, which integrates sub-band and full-band modules. FSBNet consists of an encoder, multiple full-band and sub-band blocks (FSB blocks), and a decoder. The FSB block features a sub-band module that extracts temporal information within each sub-band and computes high-level crossband dependencies through compact latent summaries, and a full-band module that captures longrange dependencies across the entire spectrogram using a self-attention mechanism. The contextual information obtained from the FSB blocks is then processed into two complex spectrograms representing the separated speech signals, which are re-synthesized into audio using the inverse short-time Fourier transform (ISTFT). Experimental results demonstrate that FSBNet achieves competitive performance compared to both time-domain and time frequency domain approaches, with significant improvements in model size reduction and processing time efficiency. Notably, this architecture outperforms most efficient time-domain models for the first time since 2019