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The experiences of bilingual Chinese international students studying in english at a small Canadian university
Get PDFThis study explores the experiences of ten Chinese students learning in English at a small
Canadian university using Linguistic Portrait Silhouettes and semi-structured interviews in a
phenomenologically-influenced case study. Five major themes were summarized: (1) The
Language Portrait Silhouette (LPS); (2) The overall experience of studying in English at a
Canadian university; (3) Transformation: Expectations, changes in expectations, changes in
motivations, biggest changes and success; (4) Resources and support; and 5) Technology
applications and learning experiences.
The Language Portrait Silhouette proved useful in understanding participants’ linguistic
identity, learning challenges, and cultural understanding. In terms of overall experience, although
the majority of participants viewed the opportunities to use English to study at a Canadian
university positively—including the cultural experience—the challenges and dilemmas faced by
students were many, including culture shock and linguistic barriers. Most participants underwent
transformations while studying in terms of motivations and expectations, with career prospects
and personal growth seen as important. Participants made extensive use of technology to support
their learning and offered suggestions for its use. Professors, for example, were expected to
provide some advice and support in their teaching to help students make better use of these
technologies.
Recommendations are made for professors, universities and future Chinese students
studying at Canadian universities. Recommendations are also made for future study
Perfectionism from inside and outside: clarifying the role of intra- and interpersonal processes in predicting maladjustment using multi-source and intensive longitudinal methods
No full textLife as a post-secondary student can be volatile and stressful, and illustrated by maladjustment
(e.g., depressive symptoms). It is critical to understand factors that increase the risk for negative
outcomes that can occur during this time. The current research examined dispositional
perfectionism and interpersonal contexts as vulnerability factors for maladjustment in
undergraduate students using multi-source and intensive longitudinal methods (e.g., daily
diaries). There is evidence that dimensions of perfectionism are specific vulnerability factors for
maladjustment especially in the presence of congruent stressors. Research also highlights the
importance of investigating the perfectionism-maladjustment relationship within interpersonal
contexts. In study 1, we tested the specific vulnerability hypothesis by assessing the extent to
which socially prescribed perfectionism and self-oriented perfectionism impacted the
stressfulness of congruent stressors (i.e., interpersonal stress and achievement stress,
respectively), resulting in maladjustment among undergraduate students. The targets (N = 296)
from study 1, identified members of their social network (i.e., influencers) to participate in study
2. Influencers (N = 720) reported on their own perfectionistic expectations to allow for an
evaluation of targets’ interpersonal contexts. Specifically, we tested an indirect effect of
perfectionistic climate (reported by influencers) on targets’ maladjustment via targets’ socially
prescribed perfectionism. We also evaluated the perfectionism social disconnection model,
which suggests that interpersonal difficulties mediate the relationship between perfectionism and
maladjustment. In study 1, the multilevel mixed models did not support the specific vulnerability
hypothesis, although daily interpersonal stress and achievement stress predicted daily depressive
affect. In study 2, path analyses showed that there were significant relations observed between
targets’ socially prescribed perfectionism and maladjustment outcomes (e.g., stress, depressive
symptoms, negative affect). There was no evidence to support the effect of the perfectionistic
climate (reported by influencers) on targets’ maladjustment. Lastly, there was partial support for
the perfectionism social disconnection model, which suggested that targets’ interpersonal
difficulties (e.g., poor social self-esteem) mediated the relationship between targets’ socially
prescribed perfectionism and depressive symptoms. Targets’ neuroticism emerged as an
independent predictor of maladjustment across the analyses, further emphasizing the need to test
models for incremental validity. This program of research addressed major methodological and
statistical gaps in the literature and helped to inform strategies for prevention and intervention
with undergraduate students who might be struggling with unrealistic pressures for perfection
and adjustment difficulties. Specifically, a multilevel approach (e.g., individual, institutional)
that emphasizes early mental health literacy, mental health curriculum in schools, individual
intervention, and education for caregivers and professionals in educational and workplace
settings is presented
A comparison and analysis of explainable clinical decision making using white box and black box models
Get PDFExplainability is a crucial element of machine learning-based making in high stake
scenarios such as risk assessment in criminal justice [80], climate modeling [79], disaster
response [82], education [81] and critical care. There currently exists a performance
tradeoff between low-complexity machine learning models capable of making predictions
that are inherently interpretable (white box) to a human, and cutting-edge high
complexity (black box) models are not readily interpretable.
In this thesis we first aim to assess the reliability of the predictions made by black box
models. We train a series of machine learning models on an ICU (Intensive Care Unit)
outcome prediction task on the MIMIC III dataset. We perform a comparison of the
predictions made by white box models and their black box counterparts by contrasting
explainable model feature coefficients/importances to feature importance values generated
by a post-hoc SHAP (SHapley Additive exPlanation) values. We then validate our results
with a panel of clinical experts. The first study shows that both black box and white box
models prioritize clinically relevant variables when making outcome predictions. Higher
performing models showed prioritizations to more clinically relevant variables than lower
performing models. The black box models show better overall performance than the white
box models. [...
Effects of forest equipment on boreal forest soils: a review
Get PDFSoil disturbance is an important aspect of forest harvesting operations. Machines that are responsible for the harvesting of trees and wood transportation have a direct effect on the soil that they operate on. Some of these machines can weigh dozens of tonnes, making their effect on the soil considerable; the degree of contact with soil also affects soil integrity. On improperly constructed roads and sensitive soils, these machines are a detriment to not just the soil itself, but the plants and wildlife that reside in the soil play dynamic roles cycling nutrients and organic matter and maintaining the ecology in forest ecosystems. Machine effects on boreal soil have been characterized and synthesized using a literature-review based approach, mainly focusing on western Canada and Ontario
Plant diversity effects on soil Collembola in boreal forest
Get PDFCollembola are one of the most abundant soil fauna in terrestrial ecosystems. They play essential
roles in ecosystem processes like litter decomposition. Ongoing biodiversity loss across taxa
harms the stability and resilience of ecosystems and therefore threatens our sustainable
development. Recent evidence has shown that biodiversity loss negatively impacts ecosystem
processes and functions such as productivity, soil microbes, and the production of fine roots.
Despite the critical importance of soil Collembola, our understanding of the effects of plant
diversity on soil Collembola remains uncertain. The purpose of this dissertation is first to
summarize previous studies and reveal the general response of Collembola to plant species
diversity across ecosystems. The second objective is to test whether tree mixtures affect the
Collembola community in young boreal forests and if these mixture effects change with water
conditions and stand ages.
In my first study, by conducting a meta-analysis of 623 paired observations of plant
mixtures and corresponding monocultures from 40 studies, I examined the effects of plant
mixtures on soil fauna abundance and diversity across global terrestrial ecosystems and
summarized consistent responses of soil fauna to plant species diversity across soil depths,
ecosystem types, and climate conditions. I found that the diversity of soil fauna was on average
10% greater in plant mixtures than expected from corresponding monocultures. In contrast, the
abundance of fauna did not respond to plant mixtures. Importantly, plant mixture effects on both
soil fauna abundance and diversity significantly increased with plant species richness in
mixtures. Moreover, the effects of plant mixtures on soil fauna abundance increased over time in
diverse species mixtures. [...
Design of a polarization reconfigurable and frequency tunable patch antenna system on a magnetic substrate
Get PDFModern radio frequency (RF) and microwave components are continuously evolving to meet the
demands of new wireless technologies. One such demand is the ability of these components to be
agile and smart. Thus, the rationale for plethora of research in the field of reconfigurable RF
components. In this work, a patch antenna system that can be tuned for its center frequency and
reconfigured for its radiation characteristics is studied on a magnetic substrate namely yttriumiron-garnet (YIG). By integrating PIN diodes along the feed lines of the two antenna elements, one
can achieve the above stated control of polarization reconfigurability in tandem with the use of
YIG substrate for frequency tuning. The antenna elements are arranged in a manner that provides
cross-polarization between them that helps to generate two different linear polarization (one for
each antenna). At the same time, the feed line is designed to provide a 90 of phase difference
between the antenna elements, thus resulting in a circular polarization when both the antennas are
activated. The simulated results of the antenna show −14.15 matching at 7.3 GHz with stable
radiation performance for three different polarizations that is circular polarization, Linear
polarization along x-axis and Linear polarization along y-axis. This is accomplished by toggling
the PIN diodes as needed. Furthermore, the antenna system is magnetized in simulations to study
its impedance and radiation response for all three polarizations. A tunability of 1 GHz is achieved
using full-wave simulations which demonstrate a range of 14%. These initial results demonstrate
the feasibility of using the proposed design concept in current and future wireless communication
systems
Enhancing en-route electric vehicle charging services with AI integration: a collaborative fog-based strategy for optimizing sustainable transportation
Get PDFIn the emergence of greener transportation, Electric Vehicles (EVs) play an important
role, expected to outnumber conventional vehicles in the near future. However, the
installation of Fixed Charging Stations (FCSs) is not keeping up with the increased
demand, especially outside urban centers. Such a challenge is prohibiting many users
from owning EVs because of range anxiety. This thesis proposes a novel cooperative
mechanism where EVs can access charging services such as Vehicle-to-Vehicle (V2V)
charging schemes, private smart Home Charging Station (HCS), or Mobile Charging
Station (MCS) to complement existing FCS services in certain regions. To this end, the
proposed mechanism divides each region into geographically distributed zones managed
by cloud-fog nodes for charging service coordination. In each zone, we employ the Hungarian matching algorithm to optimally match EVs with the available charging services.
Unlike recent approaches that establish a one-to-one matching between supplier EVs and
demanding EVs, our mechanism matches multiple demanding EVs to charging services
with a larger capacity to maximize the service offering. Comparing results with existing studies shows that our model outperforms prior approaches across critical factors.
Furthermore, our proposed matching algorithm prioritizes EVs requiring charge based
on their maximum travel range given their current State of Charge (SoC). To address
the challenge of accurately estimating EV driving range, we introduce an ensemblebased Machine Learning (ML) model offering a compelling solution for enhancing the
estimation of EV driving range for practical applications
Exploring supports for students’ complex climate emotions through interviews with Ontario Secondary Teachers
Get PDFYoung people are most vulnerable to the impacts of climate change and grapple with a
range of challenging emotions regarding climate change. Climate change education aims to
increase knowledge and engagement in climate action but to date, has given limited attention to
the emotions brought on by experiencing, witnessing, learning about climate change, and/or the
lack of government climate action or policies that will protect young people’s futures. Attention
to the affective domain of climate change education is particularly urgent, as the direct and
indirect impacts of climate change, such as the 2023 Canadian wildfires, become more salient,
and more teachers cover topics related to climate change. Through online interviews, this study
explored Ontario secondary school teachers’ (n=6) experiences and the strategies that they use
when interacting with, responding to, and supporting students’ complex climate emotions. Using
qualitative thematic analysis, teacher participants report frequently interacting with students'
complex climate emotions and feeling comfortable dealing with them, yet they also express a
sense of isolation when addressing climate change among their colleagues. Despite this, they
employ a variety of strategies to support students in navigating these emotions. Findings
underscore the necessity for a collaborative effort and additional professional development to
adequately support students' complex climate emotions. By synthesizing teacher strategies and
existing literature, I introduce an adapted guide that provides practical guidance for educators
addressing the emotional aspects of climate change in their teaching practices
Advancing precision agronomy for minimizing production risk
Get PDFFarming in Northwestern Ontario faces unique challenges, including a shorter growing season,
severe weather conditions, and limited infrastructure and support services. Despite these obstacles,
the region holds great potential for expanding agricultural production, particularly for crops like
soybeans. Soybean, a crop of significant economic and nutritional value, is susceptible to pests,
diseases, and environmental stresses that reduce productivity. Effective health monitoring is
crucial to optimize yields and quality. This study explored the use of low-cost proximal field
cameras and remote sensing techniques for monitoring soybean leaf chlorophyll. A Mapir
Survey3W camera was selected to capture high spatial resolution images in the green, red, and
near-infrared regions of the electromagnetic spectrum. The optimal camera setup was investigated
by comparing vertical (90º) and oblique (45º) orientation angles and automating image capture
using a Raspberry Pi 4 Model B powered by a solar panel system. The vertical camera showed
higher spectral reflectance values, while no significant difference was detected for vegetation
indices. Once a series of images were captured using the identified optimal camera configurations,
the images were preprocessed to obtain spectral reflectance values. Vegetation indices, such as the
Green Normalized Difference Vegetation Index (GNDVI), were calculated from the captured
images over the growing season. For calibration and validation purposes, at each field visit (within
7-10 days time), soybean leaf chlorophyll content (LCC) was measured using Apogee Instruments
MC-100 Chlorophyll Meter. The correlation between GNDVI and LCC was established over time
using the inverse function of piecewise linear regressions. The robustness of the regression models
was measured by a Kolmogorov–Smirnov statistical comparison test between the predicted LCC
over time and the field-measured LCC. The results were statistically not significant, indicating the
similarity between the two data sets. Finally, a user-friendly prototype software application was
built to make the proposed model accessible to the public. This study provided valuable insights
into the optimal setup of field cameras and the use of low-cost remote sensing techniques for
soybean leaf chlorophyll monitoring. The proposed methodologies and analyses contribute to the
remote sensing techniques in agriculture using affordable sensors, supporting sustainable
agriculture practices, and minimizing production risks in soybean cultivation
