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Exploring Lived Experience: A Phenomenological Inquiry into the Perspectives of Marginalized Students and Their Instructors with Hybrid Problem-Based Learning (PBL) in STEM Education through a Critical Digital Pedagogy Lens
No full textDespite the potential of hybrid Problem-Based Learning (PBL) to enhance accessibility and engagement in science, technology, engineering and mathematics (STEM) education, few studies have specifically investigated its effectiveness in promoting Equity, Diversity, and Inclusion (EDI) for marginalized students. This phenomenological study explores how its blended learning format shapes the experiences and perceptions of both underrepresented minority students and their instructors. Grounded in critical digital pedagogy, the study examined 40 semi-structured interviews and two focus group discussions with 29 STEM students and 11 STEM instructors at a large Canadian university. Through thematic analysis, the data yielded ten key themes that highlight the various complexities of implementing and experiencing hybrid PBL. On the one hand, there is the increased flexibility that goes along with hybrid PBL in so many ways; on the other, there is another digital divide that limits full participation from students of lower socioeconomic backgrounds cohesively. These barriers make them entirely dependent on school resources, while others struggle to keep up with coursework.
The critical but complex role of the teacher in creating an inclusive atmosphere in hybrid PBL is also pointed out in the research. On one hand, the teachers are valued as “Cultural Guide” responsible for breaking biases and facilitating inclusion for diverse learners, but on the other hand, the integration of technology adds many layers of complexity to EDI. Technology acts as a “double-edged sword”: both empowerment and marginalization of students entrench socio-economic polarities. Further on the complexity, the socioeconomic inequalities persist in hybrid PBL and influence the problem-solving approaches of students, the access to resources, and even their sense of belonging. What the difference shows is that instructors really have to be aware of these socioeconomic factors as they design PBL activities and work in groups. The study also shows that, even in the context of hybrid PBL, the gender stereotype persists: female students report that they are being pushed to non-technical roles and experiencing microaggressions. While students enjoy hybrid learning for its flexibility and accessibility, especially those with very diverse commitments, the level of digital literacy among both instructors and students is still very important to fully maximize its potential and avoid creating a situation of digital exclusion.
These findings therefore suggest that while hybrid PBL has the potential to advance EDI within STEM education, its success involves a holistic approach that includes several key factors, from digital equity and culturally responsive training of instructors to support of individual students and critical consideration of technology use as part of power dynamics. The recent pandemic has further shown its applicability beyond emergency remote teaching, indicating that PBL could still remain a useful method in physical classrooms post-pandemic. By recognizing the interplay among pedagogy, technology, instructor awareness, and socioeconomic dynamics, educators can take meaningful steps toward fostering inclusive and equitable learning opportunities in STEM for all students
Development of a novel nonlinear model and control strategy for soft continuum robots featuring hard magnetoactive elastomers
No full textMagnetoactive soft continuum robots (MSCRs), capable of controllable steering and navigation, hold substantial promise for healthcare applications. However, advancements in MSCRs have been hindered by a limited understanding of MSCR dynamics and a lack of effective control methods. Addressing these gaps, this study presents a novel, time-dependent, and computationally efficient analytical model of MSCR, alongside a new optimal closed-loop control strategy for precise high-frequency trajectory tracking. A finite element (FE) model of the MSCR is initially developed, with its validity confirmed through rigorous laboratory measurements. Using the formulated FE model, a new and computationally efficient analytical model is subsequently developed to accurately predict the highly nonlinear response of MSCR. This model operates as a system of switched linear models, each of which is a reduced-order version of its corresponding high-order linear model extracted from the FE analysis. This innovative approach not only maintains the predictive accuracy of the FE model but also significantly reduces computational demands, operating in just a few seconds. The results highlight that the developed model can accurately predict the dynamic responses of the MSCR while significantly reducing the computational load by almost 80 orders of magnitude compared with the FE model on the same simulation platform. The proposed model has been effectively utilized to develop a novel optimal control strategy using the feedforward interval type-2 fractional-order fuzzy-PID method. A hardware-in-the-loop experimental test has been finally designed to demonstrate the superior performance of the MSCR under the proposed controller
An Experimental Study on the Dynamic Hysteresis Behavior of a Magnetic Soft Continuum Robot Submerged in a Fluid Environment
Get PDFIn recent years, Magnetic Soft Continuum Robots (MSCRs) have gained significant attention partly due to their promising applications in sensitive and precise biomedical tasks, such as targeted drug delivery and minimally invasive treatments. These applications benefit from the unique multimodal locomotion of MSCRs and their wireless actuation capabilities. MSCRs typically undergo large deformation when subjected to magnetic fields. However, there are very limited studies on characterizing their nonlinear hysteresis behavior, especially in fluid environments subjected to varying magnetic field. This study attempts to experimentally investigate the real-time nonlinear and hysteretic behaviors of an MSCR (designed as a cantilever beam made of magnetoactive elastomer) fully submerged in a fluid environment. The MSCR's behavior is explored under both DC and AC magnetic intensities. In AC scenarios, the robot experiences a high oscillatory magnetic field, ranging from 5 to 25 mT, and is subjected to frequencies as high as 5 Hz. To generate a uniform and precise magnetic field, a hardware-in-the-loop experiment employing a feedforward-PID controller was designed. The experimental data were subsequently analyzed to realize the MSCR's response time histories and hysteretic responses. Keywords-magnetic soft robot; feedforward-PID control algorithm; hardware-in-the-loop experiment; large deformation; high-frequency oscillatory movements
Multizone Modeling of Airborne Quanta Transmission and CO2-based Ventilation Designs for Assessing Indoor Exposures
Get PDFIn indoor environments, ventilation is essential for diluting or removing contaminants, pathogens, excess heat, and moisture, thereby ensuring a healthy and comfortable space. The COVID-19 pandemic underscored the critical role of ventilation in controlling airborne respiratory infections indoors. During this period, inadequate ventilation systems and improper operations in densely populated public spaces were frequently linked to outbreaks and superspreading events, heightening concerns over indoor exposure risks for occupants. As COVID-19 restrictions begin to relax globally, the focus is transitioning to long-term management strategies for the virus. This transition necessitates a comprehensive understanding of the specific ventilation requirements for various indoor spaces. It is imperative to swiftly and accurately assess ventilation conditions and consistently ensure an adequate supply of clean air. This study focuses on mitigation strategies to reduce indoor exposure risks and prepare for the post-pandemic era. The multizone CONTAM modeling of aerosol transport under different mechanical mitigation strategies was investigated in five DOE prototype buildings. To utilize field evidence for improving indoor air quality, a novel approach integrating Bayesian inference and stochastic CO2 grey-box models was applied. This approach was used to evaluate the ventilation conditions within two primary school classrooms in Montreal. The Equivalent Clean Airflow Rate (ECAi) was calculated following ASHRAE 241, revealing an insufficient clean air supply in both classrooms. To achieve a sufficient ECAi, an additional 0.38 m3/s of clean air delivery rate (CADR) from air-cleaning devices is recommended. Finally, steady-state CO2 thresholds (Climit, Ctarget, and Cideal) were established to indicate when ECAi requirements could be achieved under various mitigation strategies
Doubly Selective Channel Estimation Techniques for the Next Generation Wireless Communication Systems
No full textIn next-generation wireless communication systems, such as 5G and beyond, channel estimation plays a vital role in ensuring robust and high-performance communication. Given the increasing complexity of wireless environments—characterized by high mobility, massive antenna arrays, and rapidly changing channels—accurate channel estimation is crucial for tasks such as beamforming, spatial multiplexing, interference management, signal detection, and equalization. This thesis focuses on developing novel algorithms for estimating doubly selective channels in MIMO systems and is divided into three parts.
The first part introduces three compressive sensing (CS)-based algorithms for channel estimation in millimeter wave (mmWave) hybrid MIMO systems. These algorithms utilize the basis expansion model (BEM) to efficiently capture the channel’s time variations while significantly reducing the number of unknown channel parameters. The first algorithm is adaptable to various training sequence structures, offering flexibility, while the second and third algorithms use specialized training sequences to reduce computational complexity.
The second part shifts focus to MIMO OTFS systems, recognized for their robustness against Doppler effects and delay spreads. A new row-block sparse formulation is introduced for channel estimation in the delay-Doppler domain, allowing for efficient handling of the MIMO channel matrix by grouping non-zero entries into row blocks. A row-block OMP (RBOMP) algorithm is then applied, enhancing both the accuracy and computational efficiency of the estimation process.
The final part presents another novel doubly selective channel estimation scheme for MIMO OTFS systems, leveraging a two-dimensional discrete prolate spheroidal basis expansion model (2D DPS-BEM). This model provides a more efficient representation of the MIMO channel in the delay-Doppler domain, reducing the number of unknown parameters needed for estimation. Unlike existing CS-based methods, which require prior knowledge of the number of propagation paths, this method relies only on the channel's maximum Doppler and delay shifts, significantly lowering computational complexity. Additionally, a low-overhead pilot scheme is introduced to capture temporal channel variations more efficiently, further enhancing estimation performance
Who Hath Made Man’s Mouth: Exploring Autism Representation and its Eugenic Connections to Criminality Within the Realm of Film and Media Culture
Get PDFThis thesis explores the putative correlations between the modes of criminality and its correlations of historically depicting autism on screen in a negative light. By analysing selected pro eugenic propaganda films, the essay argues there has been a consistent pathology which has over time developed into a caricature common described as the “creep”. The Thesis demonstrates how this unique trope continues to negatively influence the way we see autism in films and media by exploring different film representations which involve autistic coded characters
Integrating Vision-Language Models with Reinforcement Learning for Human-Aligned Decision-Making of Autonomous Vehicles
Get PDFThis thesis focuses on developing a new approach for improving the decision-making process
for autonomous vehicles (AVs) in complex urban driving scenarios, particularly at unsignalized
intersections, using the reinforcement learning (RL) framework. One of the primary difficulties
in RL environments is designing a suitable reward model, which can often be challenging to
achieve manually due to the complexity of the interactions and the driving scenarios. To address
this challenge, this work utilizes Vision-Language Models (VLMs), particularly CLIP (Contrastive
Language-Image Pretraining), to build an additional reward model based on visual and textual cues.
CLIP’s ability to align image and text embeddings provides unique features for translating humanlike
instructions into reward signals to guide the AV’s decision-making process. We apply two RL
algorithms, Proximal Policy Optimization (PPO) and Deep Q-Network (DQN), to train an agent in
complex unsignalized intersection environments. The performance of these algorithms is compared
with and without the CLIP-based reward model, which highlights the impact of CLIP on the agent’s
ability to learn and optimize its behavior in a way that aligns with desired driving actions. This
study’s results show VLMs’ capabilities in improving RL-based decision-making in autonomous
driving. We utilize the Highway-env simulation package, built on the OpenAI Gym framework, to
test and validate the effectiveness of the proposed framework. Our simulation experiments indicate
the framework’s effectiveness in optimizing traffic flow, minimizing collisions, and balancing both
individual and collective benefits among road users. The results highlight the potential of integrating
VLMs to provide human-aligned instructions, which could guide autonomous vehicle actions
toward safer and more socially acceptable behaviors and eventually promote AVs’ safe and trustable
deployment in future intelligent transportation systems
The Mask and the Veil: Industrial Carnivals and the Theatrics of Social Control in 19th century St. Louis
Get PDFThis thesis links the Veiled Prophet Society, a covert organization formed in 1878 by St. Louis' financial elite in response to the Great Strike of 1877, to the discursive transformation of procession into a “top-down” modality of power. Depicted in the Missouri Republican with a rifle and a bright pointed hood, the Veiled Prophet presaged the Ku Klux Klan’s later iconography, suggesting an intentional message of racial control. Accordingly, the organization’s procession aimed to indoctrinate Black and low-income communities, promoting the maxims of a burgeoning free market economy at a time of unparalleled economic disparity. This thesis thus situates the Veiled Prophet Society within a broader historical context, tracing its roots to European folk traditions like Charivari while examining its links to other American fraternal organizations, such as the Ku Klux Klan and Krewes of Mardi Gras. By appropriating and transmuting carnivalesque folk forms into a vehicle for ideological transmission, the Veiled Prophet Society sought to reinforce racial boundaries and divide the working class amid economic unrest. The thesis draws on Cedric J. Robinson's concept of "racial capitalism" and Dubois’ “counter revolution of property” to reveal how market concerns reshaped racial constructs. Additionally, my research addresses the dual nature of secrecy and spectacle in right- wing organizations. While early scholarship viewed Klan secrecy as a necessary limitation, more recent studies suggest it functioned to attract new members and propagate ideology. This thesis advances the understanding of how "top-down" processional forms, like those of the Veiled Prophet Society, exploited the duality of secrecy and spectacle to control social narratives and maintain economic dominance, infusing processional traditions with a distinct panoptic quality
A Novel Hybrid Model for Electricity Price Forecasting Based on the Integration of Bi-directional Long Short-Term Memory and Gated Recurrent Unit
Get PDFThe prediction of electricity prices plays a pivotal role in the wholesale electricity markets, influencing sale prices, bidding strategies, electricity dispatch, control, and the management of market. Notably, forecasting in a deregulated electricity market is challenging due to multiple factors such as high volatility, non-stationarity and multi-seasonality of electricity prices. In response to this challenge, this research proposes a novel hybrid deep learning model employing Bi-directional Long Short-Term Memory (Bi_LSTM) and Gated Recurrent Unit (GRU) for real-time electricity price forecasting. In this model, the output sequences from the Bi_LSTM layer, which captures both past and future temporal dependencies, are directly fed into the GRU layer to refine the feature extraction. This hybrid approach not only reduces overfitting risk of a single model, but also increases robustness and adaptability of model. Three studies are conducted in New York City (NYC), electricity market to evaluate the model by systematically comparing the obtained results. First, the proposed model, Bi_LSTM-GRU, outperforms several baseline models, spanning a statistical time-series method: Auto Regressive Integrated Moving Average (ARIMA), Machine Learning approaches: Linear Regression (LR), Random Forest (RF), eXtreme Gradient Boosting (XGB), and Support Vector Regression (SVR), and Deep Learning techniques: Long Short-Term Memory (LSTM), Bi-LSTM, GRU, and Convolutional Neural Network (CNN). Secondly, the possibility of hybridizing CNN and Recurrent Neural Network (RNN) architectures has been examined. The proposed model also surpasses CNN-LSTM, CNN-Bi-LSTM, and CNN-GRU. Lastly, the potential contribution of data decomposition techniques in enhancing the proposed model has been assessed. It is found out that adding Wavelet Transform (WT) or Fourrier Transform (FT) to decompose the data leads to higher error rates
Is white matter the weakest link? - Early detection of white matter changes with MRI
Get PDFWhite matter (WM) tracts play a crucial role in enabling efficient neural transmission, which is essential for optimal brain function. Once overlooked, WM is now recognized for its constant remodeling throughout life, responding to both enriching and adverse factors. These alterations in WM microstructure can enhance cognitive and motor performance through more efficient transmission, or conversely, contribute to functional decline. Notably, WM changes are among the earliest alterations observed in neurodegenerative disorders such as Alzheimer’s disease (AD) and other dementias, highlighting the potential for WM as a target for early interventions.
However, our current knowledge is limited regarding: 1) the time scales at which plastic changes occur and 2) the biological mechanisms driving microstructural changes. This is largely due to the physiological non-specificity of commonly used neuroimaging techniques and the predominantly univariate focus of most studies in the field.
This Ph.D. thesis presents four original studies focused on investigating early WM changes in health and disease. The first study examines longitudinal plastic changes in WM following short-term motor learning in young, healthy participants, providing insights into activity-dependent WM remodeling. The second study introduces MVComp, an open-source toolbox developed to compute a multivariate distance metric—the Mahalanobis distance (D2). MVComp allows the integration of various imaging features, yielding individualized scores of deviation from a reference.
The latter half of the thesis focused on the investigation of early pathological changes in WM among older adults at risk of dementia, using the multivariate framework developed in study two. The third study explored the relationship between WM alterations and cardiometabolic risk factors in older adults with a family history of AD. Individuals at higher genetic risk of AD (Apolipoprotein E (ApoE) ε4) displayed a distinct pattern where LDL-cholesterol negatively impacted WM health, with myelination changes as the primary underlying mechanism. Finally, the fourth study assessed WM deviations in coronary artery disease patients, linking higher D2 scores in specific arterial territories to lower fitness levels and poorer cognition.
Together, these findings underscore the dynamic nature of WM changes and demonstrate that multivariate approaches offer a comprehensive characterization, shedding light on the biological mechanisms at play