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The silence that followed Indian Residential Schools: sharing our stories and reconnecting oral history among Omushkego Cree family members in Ontario
Full text linkFor many Indian Residential School (IRS) survivors, there is a pervasive silence
surrounding their childhood experiences. The first research question, what childhood
stories pre-existed Indian Residential Schools for Omushkego Elders and community
members in Northern Ontario, unearthed childhood experiences in order to (re)animate
oral storytelling and cultural practices within Omushkego communities in Ontario that
were systematically eliminated/reduced for IRS survivors during their school years. The
second research question, what Omushkego cultural knowledge and/or themes can we
(re)learn and (re)claim from these stories and storytelling experiences with Omushkego
Elders and community members, explored the various impacts of (re)claiming oral
storytelling and cultural practices for IRS and intergenerational survivors from Northern
Ontario, as well as examined common themes and storytelling practices among the
collected Omushkego stories. The last two questions, what are some key outcomes for
individual Omushkego community members when they have shared and (re)created
oral storytelling and language cultural practices within our community, and how can
Omushkego people identify and assert cultural reclamation in our lives and work as
Omushkego people in Ontario, and by extension, Canada, highlighted cultural and
identity affirmation through storytelling and confirms that healing opportunities can take
place during these processes for Elders and community members who lost storytelling
and cultural practices because of IRS experiences. This project included three
Omushkego women who are from the Hudson Bay Lowlands and were born between
1933 and 1954, as well as me as an intergenerational survivor of Residential Schools
and ongoing colonization. I used storytelling methodologies, Kovach’s (2010)
conversational method, sharing circles and Indigenous epistemologies to guide my practical and ethical choices. I relied heavily on Indigenous ways of knowing and an
Indigenous informed autoethnographic approach. Therefore, I am included within this
document through my own stories, my reflections, and my actions which assert my
Omushkego Cree identity right from the beginning through to the end. I am not alone
and follow in the footsteps of Indigenous scholars who feel the need to situate our
selves within research
Ontario youth mental health literacy and social determinants
No full textMental health literacy includes: recognizing mental health problems and illnesses; knowing how
to locate accurate mental health information and professional help; risk factors and causes of
mental health problems or illnesses; self-help techniques; promoting help-seeking; and the ability
to build and maintain good mental health (Jorm et al., 1997; Kutcher & Wei, 2015; Kutcher et
al., 2016; Marinucci, Grové & Allen, 2022). Currently, the Ontario Health and Physical
Education curriculum requires the introduction of mental health literacy topics however, students
are only required to take one credit in Health and Physical Education, therefore, they are not
receiving most of the mental health curriculum. Therefore, what do Ontario youth know about
mental health and how do they obtain mental health information? What do Ontario youth want to
know about mental and how would they like that information disseminated? What gaps currently
exist in mental health literacy programs? Methods: An online survey with demographic and
open-ended questions about mental health, mental illness and the social determinants of health.
Results: Ontario youth are not satisfied with the education they received about mental health, and
mental health problems or illnesses. They also believe that school is a good place to learn about
mental health, and would like interactive workshops to disseminate the information
Exploring how the Dark Tetrad is associated with coping: An intensive longitudinal daily diary approach
No full textPersonality is broadly defined as a stable and enduring configuration of cognitions, emotions,
and behaviours that influence how an individual experiences everyday life. The Dark Tetrad
describes a cluster of subclinical and socially aversive, “dark” personality traits (i.e.,
Psychopathy, Narcissism, Machiavellianism, and Sadism). Personality, including varying
degrees of dark personality traits, can influence the way that one copes. Coping consists of
methods that one employs to deal with stressors or their associated emotional responses, and
these methods can be adaptive or maladaptive. Although much research has examined how
individuals higher in Dark Tetrad traits react to and experience stress, less research has been
conducted directly examining how they cope with stress. The current study sought to evaluate
and clarify how individuals higher in Dark Tetrad traits cope with daily stressors and to address
stark methodological gaps in the literature. It was hypothesized that those with higher levels of
Dark Tetrad traits would endorse greater maladaptive coping strategies (i.e., emotion-focused
and avoidant/disengaged coping) in stressful daily situations compared to those with lower levels
of Dark Tetrad traits. Undergraduates (N=359) were recruited for an intensive longitudinal (daily
diary) study. Participants completed self-report measures on baseline personality, followed by a
short daily survey each day for 14 days that evaluated stressors experienced over the last day and
the methods that participants used to cope with them. Multilevel regression analyses revealed
that hypotheses were generally supported, such that individuals higher in Psychopathy,
Machiavellianism, and Sadism endorsed more emotion-focused and avoidant/disengaged coping,
whereas, those higher in Narcissism endorsed a combination of all types of coping (both adaptive
and maladaptive). However, there were very few interactions between personality and daily
stress to predict coping. [...
Artificial intelligence-based control schemes for robust and sustainable wind energy conversion system
Full text linkTo reduce fossil fuel consumption, which causes carbon dioxide emissions and global
warming, renewable energy is gaining popularity. Among various renewable energy sources, wind
energy is one of the most cost-effective ways to generate electricity. Numerous studies have been
conducted to improve the performance of wind energy conversion systems (WECS) in various
aspects. However, traditional control strategies employed in WECS often lead to lower efficiency,
complicated implementation, complex system modeling, sophisticated drive circuit design, and
suboptimal responses. This PhD thesis presents a comprehensive exploration of cutting-edge
techniques for optimizing wind energy conversion systems, unified by the application of a
proposed multi-agent reinforcement learning (MARL) method. The research is structured around
three primary objectives, each contributing to the advancement of renewable energy technologies
through the innovative use of MARL. Firstly, the thesis delves into the control of a neutral point
clamped (NPC) power converter employed in a direct-drive permanent magnet synchronous
generator (PMSG)-based WECS. The focus is on enhancing power quality and meeting grid code
requirements for total harmonic distortion (THD). Traditional controllers like PI often struggle
with parameter tuning and adaptability to varying operating conditions, resulting in suboptimal
performance under dynamic and unbalanced scenarios. AI-based approaches, while more adaptive,
typically require extensive offline training and detailed system modeling, making them less
practical for real-time applications. The proposed approach eliminates the need for offline training
and extensive system modeling, distinguishing itself from traditional machine learning (ML),
neural network-based techniques, and PI-based methods. Through simulations and comparative
analysis, the effectiveness of the MARL strategy is validated, particularly in handling unbalanced
voltage sag scenarios. The integration of meta-learning to optimize the discount factor (DF), a vital
hyperparameter in RL-based approaches, further enhances the adaptability and convergence rate
of the control system, ensuring power quality. Afterwards, the research addresses the challenges
in maximum power point tracking (MPPT) for the wind energy conversion systems. Traditional
methods like Perturb and Observe (P&O) and incremental conductance are known for their slow
dynamic response and susceptibility to steady-state oscillations around the maximum power point,
especially under rapidly changing wind conditions. The proposed customized MARL approach
overcomes these limitations by employing multiple agents that work collaboratively, resulting in
improved energy output and responsiveness to wind speed variations. [...
Texture classification on uneven surfaces using deep learning techniques
Full text linkRobots are increasingly essential in various fields, excelling in tasks from routine operations
to hazardous situations. Enhancing robots with human-like capabilities, such as tactile
sensing, broadens their potential applications. Tactile sensors enable robots to perceive and
interact with their environment similarly to humans. This research focuses on leveraging
tactile sensors to classify textures on uneven surfaces, an area previously unexplored in the
literature. By collecting data points along predefined paths on object surfaces, we minimized
assumptions about the object’s geometry, making the system more flexible and adaptable.
These data points guided the robot’s trajectory, during which tactile data were systematically
gathered on the surface of uneven objects, marking a pioneering effort in this area.
To improve texture classification and reduce processing time, we employed a sliding
window approach, segmenting the dataset into smaller overlapping windows for multi-scale
analysis. In addition to data from uneven surfaces, we supplemented our dataset with tactile
data from even surfaces from another study. We applied advanced deep learning models,
including convolutional neural networks (1D CNN), recurrent neural networks (bidirectional
LSTM), and hybrid architectures, to classify tactile textures using time-series data. The
models achieved average accuracy, precision, and recall rates of 92.3%, 92.4%, and 92.3% for
uneven surfaces, and 96.9%, 97.0%, and 97.0% for even surfaces.
This study demonstrates the importance of tactile sensing in robotic systems, particularly
for texture classification on uneven surfaces. By incorporating MARG and barometer sensors
into the Open Manipulator X, this research advances tactile perception in robotics, equipping
robots to interact more effectively with diverse environments. The findings set the stage for
future applications where precise tactile perception is essential
Short-term responsive mating intentions increase with estradiol and testosterone across the menstrual cycle: scale development and an observational study
Full text linkThe extent to which estradiol, progesterone, and testosterone influence mating behavior
across the menstrual cycle is unclear. The Proactive and Responsive Mating Strategies Scales
(PARMSS) were developed to separately examine two specific components of sexuality and were
used here to test divergent hormonal associations. Preliminary psychometric data (N = 364 females)
suggest that both scales consist of one factor and demonstrate strong psychometric properties (internal
consistency, test–retest reliability, and construct and convergent validity). The PARMSS were
used in a repeated-measures observational study to examine the relationships between changes in
endogenous hormone levels and both proactive and responsive mating intentions with potential new
short-term or long-term partners in healthy pre-menopausal participants (N = 38). At two points
in their cycle, participants provided salivary hormone samples in the laboratory and reported the
likelihood of engaging in proactive and responsive behaviors with men in photos and vignettes.
Participants reported greater responsive than proactive intentions. Increases in estradiol and testosterone
across the cycle were associated with increases in short-term mating intentions, particularly
responsivity to potential short-term relationship partners. No associations were found for intentions
that were proactive or that involved potential long-term partners or for progesterone. Changes in the
three hormones explained changes in short-term responsive mating intentions (22% of the variance).
The results suggest (a) cyclical changes in estradiol and testosterone are differentially associated
with changes in responsive vs. proactive mating intentions and (b) context-dependent changes (i.e.,
short-term vs. long-term mating intentions and possibly relationship status). The findings require
replication with larger and diverse samples
Shifting grounds: the rise, fall, and resurgence of the towns of Atikokan and Oloibiri
No full textThis dissertation investigates the transformations of the towns of Atikokan in Canada and
Oloibiri in Nigeria, both transitioning from reliance on extractive industries. Focusing mainly on
their social and economic challenges and opportunities, it highlights the distinct paths each has
taken toward resilience and adaptation in the wake of industrial decline. By employing a mixedmethods approach, including interviews with community leaders, residents, stakeholders, and
politicians, the research offers an understanding of how these towns have navigated their postindustrial realities.
The towns of Atikokan and Oloibiri, each with a unique history linked to extractive practices,
provide contrasting narratives. Atikokan’s shift from iron mining to a diversified economy
showcases the benefits of proactive planning and strong community engagement, while Oloibiri's
experience with economic hardship and environmental challenges post-oil depletion underscores
the dangers of over-reliance on a single resource. This contrast highlights the importance of
strategic diversification and environmental stewardship. [...
Effect of enhanced efficiency nitrogen fertilizers and ANVOL™ on spring wheat production and soil health
Full text linkNitrogen (N) is an essential macronutrient that plays a critical role in the cultivation of
spring wheat, affecting several physiological and developmental processes. The widespread
use of N fertilizers can result in environmental contamination, as approximately half of the N
applied as fertilizers is lost through various pathways. Urea treated with N stabilizers such as
urease inhibitors and nitrification inhibitors could be an effective way to reduce N losses. I
hypothesized that application of enhanced efficiency N fertilizers such as polymer-coated
urea and urea supplemented with inhibitors of urease and nitrification will improve the
growth, yield, and quality of spring wheat, outperforming the traditional application of
untreated urea. This study tracked the effects of different N sources at two different
application rates (80 kg N ha-1 and 120 kg N ha-1
) on plant growth attributes, field
productivity, soil health metrics, and soil chemical and biological parameters. Nitrogen
source had minimal effect on soil health, with only slight changes in microbial composition
and nutrient levels. The use of either traditional urea or enhanced efficiency N fertilizers
corresponded to the development of beneficial microbial communities. Plant phenotypic
traits, grain characteristics, soil nitrate levels, and disease occurrence were not significantly
influenced by the choice of N source or application rate, an outcome that can be attributed to
limited rainfall during the growing season of the experiment. Grain yields were no higher in
any treated plots compared to the no-N reference plots. Plant assimilation of N did occur
compared to reference plots, at three times the concentration during booting and two times
during tillering stages. Overall, N management strategies that prioritize optimal nutrient
absorption, improve soil structure, and promote sustainable agricultural practices are
recommended. However, these strategies must be adapted to prevailing environmental
conditions
Policies, procedures, and guidelines: are universities effectively ensuring AI (academic integrity) in the era of generative AI?
Full text linkThe objective of this study was to analyze Generative AI guidelines and policies at
Canadian universities, examining how these universities are ensuring academic integrity in the
face of challenges posed by using Generative AI tools in academic work. Focusing on assessment
redesign, AI-content citation, and AI-detection, the study employed qualitative document analysis
of policies and guidelines from the top twenty Canadian universities according to Times Higher
Education World Rankings. This purposive sampling strategy, focused on leading institutions from
different provinces, aimed to provide a representative overview of best practices and emerging
trends in Generative AI policy and guideline development. The analysis revealed both
commonalities and differences in institutional approaches. While universities generally emphasize
transparency through documentation, updated academic integrity policies, and instructor
autonomy in AI use, they differ in their approaches to AI detection tools, as well as AI
acknowledgment and citation. These results show Canadian universities' varied strategies to
address the complexities of Generative AI in academic environments. The study identifies key
recommendations for instructors, students, researchers, and staff, offering a foundation for
developing comprehensive Generative AI guidelines at the university level
Application of flow-through sold-phase-synthesis to the fluorescent labeling of amines with carboxylic and funtionalized bodipy dyes
Full text linkThe use of fluorophores for the labelling of biomolecules in living cells has
become a key method for understanding processes in cellular biology. Synthetic
fluorescent molecules can be introduced non-specifically to uniformly stain cells
or selectively label a protein of interest to visualize cellular activity and
metabolism using fluorescent microscopy. An increasingly popular small
molecule fluorophore at the forefront of fluorescent cellular observation is the
group known as the 4,4-difluoro-4-bora-3a,4a-diaza-s-indacene (BODIPY) dyes.
This particular family of fluorophores are known to be strongly UV-absorbing and
emit sharp fluorescent peaks with high quantum yields. Characteristics that
make BODIPY dyes even more desirable for biological imaging are their
insensitivity to the polarity and pH of their environment allowing them to stay
reasonably stable at physiological conditions. Structural modifications to the
BODIPY core allow for the fine tuning of its photochemical properties and allow
a certain level of fluorescence control – however these changes can result in
long, often low-yielding syntheses. As part of this research, a solid-phase-
synthesis (SPS) method was developed as a flow through system to efficiently
attach a variety of BODIPY fluorophores to amine-functionalized compounds. In
order for this reaction to occur, the fluorophore requires a carboxylic acid moiety
available to attach to the resin and subsequently couple to an amine through
amide bond linkage. The synthesis of these BODIPY derivatives will also be
described.
This work demonstrates an efficient method for coupling different BODIPY dyes
to a variety of amines as well as the preparation of an AMPS-DCT resin used for
amide coupling using SPS. The resulting fluorescent compounds will be tested
for fluorescent characteristics to provide further insight into the effects that
structural modification has on the fluorophore’s attractive photochemical
properties