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Accumulation of snow on plants: American elm tree
No full textElm is a deciduous tree, so its branches are bare in the winter. It has many small fine branches, which do not capture snow well. Only the larger branches accumulate snow
The Crescent of Hope
No full textThis image captures the heart of my research. I focus on rebuilding the meniscus, the crescent-shaped cartilage in the knee that is essential for joint function. Because it receives very little blood flow, the meniscus does not heal easily after injury. On the left, a 3D bioprinter represents the precise tools I use to recreate knee tissue. The gloved hands highlight the teamwork between engineers and surgeons working to improve recovery. In the center is the knee joint, where our work aims to restore damaged tissue. On the right, the glowing crescent shape represents the meniscus. It stands for both the complexity of the problem and the promise of regeneration. My research combines stem cell biology, computational modeling, microgravity studies, and single-cell genomics. By understanding how cells behave and creating personalized tissue, I aim to help the body heal itself more effectively. This image is a glimpse into the future of healing
Insight into government, August 1, 2025
No full textAlberta's independent newsletter on government & politics
Exploration of Engagement Monitoring in Video Games Using EEG System
No full textBackground: Cognitive decline and dementia prevention have been flagged by the World Health Organisation as a global mental health priority, and scholars have stressed the potential of cognitive rehabilitation games to delay the onset or progression of dementia. Inadequate engagement during video game play prevents older adults from benefiting from playing computer games. The common indicators of game engagement, such as facial expression, eye blinks and body movements, are more effective in young adults and less effective in older adults. Besides, most existing studies used multiple wearable systems to capture engagement features, which makes them impractical for use outside the laboratories. This study aims to explore the relationship between game difficulty levels, three standard EEG engagement indices and the self-reported flow state scale score during video gameplay and develop an accurate machine learning algorithm for the classification of user states into high and low engagement.
Methodology: Participants included 18 young adults (18 – 59) and 9 older adults (60 and above) who played a stunt plane video game while their EEG signals were continuously recorded using a wearable EEG headset, EPOCX. Concurrently, in-game metrics were logged to provide objective measures of performance. Three EEG engagement indices used were /(+), /,1/ where theta band is [: 4Hz – 8 Hz], alpha band [: 8 Hz – 13 Hz], and Beta is [: 13 Hz – 30 Hz]. Participants completed the Flow State Scale for Occupational Tasks questionnaire after the easy, optimal and hard level of gameplay, while Friedman’s test was used to compare scores across game levels. Sixty-five machine learning models were developed and trained to classify the participants' engagement in video gameplay into “high” and “low.” Support vector machines (SVM) and random forest (RF) classifiers were employed in the machine learning algorithm.
Results: The self-reported engagement score significantly varies with the game difficulty levels (p =0.027) and the optimal level has the highest reported engagement score. EEG engagement indices 1 and 2 did not have significant main effect on the game difficulty levels (p > 0.05) but EEG index 3 did (p = 0.001). The classification algorithm's accuracy (F1 score) was 89% and 81% for within-subject and cross-subject models, respectively, with the three EEG indices combined. The classification F1 scores for young adults and older adults' performance reports were 90% and 85%, respectively.
Conclusion: The study showed that the game’s difficulty level setup elicited higher self-reported engagement at the Optimal level than at the Easy and Hard levels. The model that combined all the EEG engagement indices as an input feature performed best, while the SVM classifier was the best classifier for EEG engagement classification. EEG data is equally effective for engagement analysis in older adults compared to young adults, though at a lower F1 score
The Implementation and Impact of Social-Emotional Learning (SEL) Programs in Early Childhood Education Curriculum Design
Get PDFSocial-emotional learning (SEL) has emerged as a transformative force in early childhood education, providing a structured framework that bridges cognitive development with emotional and interpersonal skill-building (Denham, 2006; Durlak et al., 2011; Oberle & Schonert-Reichl, 2017). A growing body of research confirms that SEL enhances children’s self-regulation, empathy, prosocial behavior, and academic performance, underscoring its centrality to holistic education (Blewitt et al., 2018; Jones & Kahn, 2017). Guided by Lewin’s Change Management Model, this critical literature review synthesizes theoretical, empirical, and cross-cultural research to examine effective SEL integration strategies, implementation challenges, and the systemic reforms necessary for sustainable SEL adoption. Findings underscore SEL’s potential to enhance emotional regulation, academic outcomes, and social competence, while systemic barriers such as structural barriers, resource constraints, limited teacher training, and cultural resistance complicate its adoption. Case studies from Canada, South Africa, and New Zealand illustrate that SEL implementation must be culturally responsive, context-specific, and equity-focused, addressing linguistic, racial, and socio-economic disparities in access and outcomes. The analysis emphasizes leadership’s role in driving sustainable integration through policy alignment, sustained professional development, culturally responsive pedagogy, and community engagement. By framing SEL as a systemic organizational change, this review contributes to ongoing debates on holistic education, decolonizing curriculum, and equitable pedagogy, offering actionable recommendations for educators, policymakers, and researchers
Blood flow manipulation in the presence of coarctation in large arteries
No full textCoarctation of the aorta (CoA) is characterized by narrowing in the thoracic aorta and can lead to poor blood distribution and flow features. Current treatment techniques are associated with long-term complications and require life-long monitoring of patients. Passive flow control techniques are underutilized in hemodynamics despite implementation in other engineering fields, and may provide a novel treatment technique for CoA. This thesis will characterize medically undesirable flow features that arise in the presence of CoA and explore flow manipulator performance for suppressing them. This thesis explores the feasibility of improving blood flow around a coarctation in rigidified arteries via flow manipulators under various configurations. Computational fluid dynamics simulations of an idealized rigid artery with coarctation are completed utilizing physiological flow conditions obtained from magnetic resonance imaging. Blood is modelled as a Newtonian fluid and Navier-Stokes equations are directly solved to obtain the flow over several cardiac cycles. Vortex shedding and regions of severely elevated oscillatory shear index (OSI) were observed in the coarctation wake. A total of 19 flow manipulator configurations were explored, which showed a positive but negligible impact on pressure drop. Flow manipulators demonstrated interaction with vortex shedding by delaying their onset and reducing vortex strength (best, 22% reduction). Thicker flow manipulators may better suppress vortex shedding but are not recommended due to additional recirculation (thickness ≥ 0.06D). A flow manipulator placed in the wake of the coarctation may better interact with the vortex shedding process but has no meaningful impact on pressure drop. Rigidity of the artery may have a significant impact on flow manipulator performance
Under the Same Sky: Echoes from a Remote Fishing Community
No full textThis photo captures the night sky above a remote fishing island in Mexico, taken during my fieldwork in the summer of 2023. The image emerged from a reflective ritual I engaged in every evening after working alongside the community. I would spend a few quiet moments listening to the ocean waves and gazing up at the stars, allowing myself to process the day’s experiences while soaking in the peaceful atmosphere. These moments offered beauty and marvel, yet also highlighted the remoteness of island life, a community that depends entirely on the sea and weather for income, food, and essential supplies. In the photo, two fellow researchers stand in stillness in front of the island’s old lighthouse, contemplating the same vast sky that connects us all. My research explores how health and well-being are shaped by climate change and the deep socio-environmental interconnectedness found in ocean-dependent communities. This photograph reflects not just a place but a feeling, a call to honour local voices and work collectively to protect both people and the ecosystems
Evaluating Mergers and Acquisitions Performance: A Gram-Charlier Approach to Performance Measurement
No full textThis thesis examines the application of performance measures to mergers and acquisitions (M&A), a field that has received surprisingly little dedicated research despite the extensive use of such measures in evaluating mutual funds and stock performance. The study aims to identify and apply the most effective performance metrics for assessing M&A success, with a particular focus on how prevailing market conditions influence the choice of appropriate metrics. A one-size-fits-all approach to M&A performance evaluation is inherently flawed, as different market environments demand different analytical tools.
Our research delves into a variety of established performance measures, analyzing their strengths and weaknesses within various market contexts, including bull markets, bear markets, and periods of high volatility. We also consider the challenges in isolating the impact of the M&A event itself from broader economic factors and industry-specific trends that influence post-merger outcomes.
To enhance the accuracy of our analyses, we utilize the Gram-Charlier expansion (GCH) to model return distributions. This method is particularly valuable in addressing the non-normality often found in M&A performance data, allowing for more robust estimations of annual returns and providing refined methods for annualizing quarterly returns. By combining theoretical frameworks with real-world examples, this thesis offers valuable insights into the dynamics of M&A performance, providing a more nuanced understanding to improve decision-making in diverse market scenarios