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Chapter 3. Additional datasets and movies
No full textData and movies associated with Chapter 3 of my thesis, The Evolution of Coordination: ATP Signaling in Sponges. These data are specific to experiments done in Bamfield, BC on Haliclona cf permolli
Unbroken Relationships
Get PDFUnbroken relationships... we all have them. Our lives are composed of relationships and to have one remain unbroken is profound. In Wood Buffalo National Park, one of the outstanding universal values that contributes to the UNESCO World Heritage Site status, is the unbroken relationship between wolves and bison. My research objective is to provide an update on their dynamics. During my fieldwork I visit sites recently occupied by wolves to see if there is a bison kill. I visit these areas days to weeks after the two
were last there and infer the dynamics of their relationship from what is left behind. This photograph captured the first time I saw them interact first hand. The bison charged down the road toward the lone wolf standing boldly in front of them. The bison came within 50 m of the wolf before the bulls moved to the front of the herd and the calves to the back. I expected the herd to run straight through the wolf; instead, they came within five meters of it, before turning abruptly into the woods. As if the wolf had been shepherding the herd, it waited until the last bison stepped off, then followed. What happened next is only known to the wolf and bison
Application of Deep Learning Models for Children’s Dental Fear and Anxiety Assessment
No full textGlobally, approximately one-third of children experience dental fear and anxiety (DFA) during early childhood. DFA can negatively impact children's emotions and dental behaviour, making them likely to exhibit treatment avoidance and exaggerated reactions to minor deviations from routine. Dental professionals should be aware of children’s DFA to prevent the development of phobias and choose suitable anxiety management techniques to ensure a positive dental experience and the delivery of effective treatment. Self-reported assessment tools are the most commonly used method for evaluating DFA. Interviews are frequently used in adults; however, young children's linguistic and cognitive limitations render them ineffective for this group. Alternatively, drawing analysis offers a non-verbal, self-reported, and open-ended method for collecting information from children. This method has been effectively used to assess children's cognitive abilities and is a valuable tool in evaluating clinical anxiety. The Children Drawing Hospital (CD: H) score sheet was initially developed for drawing analysis to assess hospitalized children’s anxiety. Adapted for dental research, multiple studies have shown a strong correlation between CD: H outcomes and other anxiety assessment methods, including behavioural tools.
Although the CD: H has demonstrated strong reliability for DFA assessment, it has limitations when applied in daily practice. It includes subjective questions, which increases the risk of rater bias. Moreover, the CD: H comprises 23 questions, making it difficult to obtain feedback quickly.
Artificial Intelligence (AI) systems have shown the potential to reduce subjectivity in decision-making and dental diagnosis while rapidly evaluating multiple features and providing immediate results. In this study, we applied AI systems to automate drawing analysis for DFA assessment in pediatric dental patients. Two approaches were implemented to achieve this.
The drawings were manually classified based on the CD: H score sheet. Then, a two-step classification with noise reduction was performed. First, a binary ResNet-18 algorithm categorized the drawings into high and low-average levels of anxiety. Then, the YOLO-v5 algorithm was trained to detect human figures. A YOLO-v8 was then utilized to classify human figures existing in the drawings, binarily to low and average anxiety levels.
The initial approach showed promising results, with the first classifier achieving a testing accuracy of 0.92, the object detector 0.64, and the final classifier 0.66. However, the method offered little insight into the algorithm's decision-making process. Moreover, while expanding the dataset was expected to enhance accuracy, data collection for this study was highly time-consuming and resource-intensive. Therefore, a second method was developed based on the pilot study.
In this method, human figures were detected, and the related features were scored, along with colour and layout specifics. The paper orientation and usage were evaluated using pixel value calculations, K-means clustering was employed to score colour-related features, and a two-step detection process followed by classification using DL algorithms for assessing human figure-related features was used.
The first detector was the aforementioned YOLO-v5 detector algorithm. Next, another YOLO-v5 model recognized faces, and then a YOLO-v8 algorithm classified the faces as happy or unhappy. The total score of all features was used to determine the final anxiety level of the drawing.
This feature scoring method showed moderate accuracy in scoring each feature; however, fuzzy logic contributed to an overall final accuracy of 0.76. Therefore, it can be concluded that artificial intelligence systems have the potential to be applied to pediatric patients' DFA assessments through automated drawing analysis
Unlearning the Monster: Teaching English language arts with an anti-colonial, feminist approach
No full textThis paper uses an autoethnographic approach to explore how educators can build solidarity with non-White students and unlearn White supremacist ontologies present in education. It critiques policies, particularly Teaching Quality Standard (TQS5) and anti-racism commitments, which position English language arts teachers as the primary agents of change in anti-racism and equity efforts, highlighting how such policies indirectly place the responsibility on educators while maintaining a hierarchical power dynamic that absolves educational boards. The paper is framed by an anti-colonial theoretical perspective, examining how the Colonial Matrix of Power (CMP), influenced by Aristotelian logic, perpetuates White supremacy in education. Feminist methodology is discussed as a tool for resisting these colonial structures and informing alternative pedagogical practices. The paper’s structure is later divided into two sections: Section A examines The Marrow Thieves (2017) by Dimaline, finding that White teachers in Alberta are
often ill-equipped to teach Indigenous literature, relying on violence-centered approaches. In response, feminist pedagogy and Indigenous-futurism are suggested as alternatives. Section B explores The Hate U Give (2015) by Thomas, critiquing colonial frameworks in anti-racism efforts and advocating for anti-colonial approaches centred around African American vernacular to disrupt embedded racism in education. The paper concludes by emphasizing the importance of journaling as a reflective practice for confronting biases and shifting pedagogical paradigms
Development of Robust Algorithms Dedicated to Four-Phase Equilibrium Calculations and Four-Phase Envelope Constructions
No full textAn aqueous phase is inevitable within the pore spaces of an oil reservoir. When both hydrocarbons and water are present, up to four phases (i.e., a vapor phase (V), a light hydrocarbon phase (L1), a heavy hydrocarbon phase (L2), and an aqueous phase (A)) can coexist at a given thermodynamic equilibrium. Thus, robust and efficient vapor-liquid-liquid-aqueous (VLLA) four-phase equilibrium calculation algorithms need to be developed to accurately capture such four-phase equilibria in situ. As the number of phases increases to four, it becomes significantly more difficult for both stability tests and flash calculations to converge. Such convergence difficulty tends to exacerbate in near-critical regions, where phase behavior changes rapidly with slight variations in pressure or temperature, and along phase boundaries.
One approach to mitigating this complexity is by reducing the dimensionality of the four-phase flash calculation problem. Based on this idea, we propose a four-phase flash calculation algorithm that leverages the free-water assumption under pressure-temperature (PT) specifications, where the aqueous phase is considered to consist of pure water only (Tang and Saha, 2003; Lapene et al., 2010; Li and Li, 2017; Pang and Li, 2017; Li and Li, 2018). Case studies show that this free-water flash calculation algorithm is more computationally efficient than the conventional full-flash algorithm while maintaining good accuracy. Building on this, we develop an isenthalpic (PH, i.e., under pressure-enthalpy specifications) four-phase equilibrium calculation algorithm using a nested approach. In this design, the PT free-water algorithm is embedded in the inner loop to solve phase fractions and compositions, while the outer loop updates temperatures using the energy conservation equation. The proposed PH algorithm is robust and derivative-free. Our testing demonstrates that the proposed PH algorithm reliably converges to the correct phase equilibria with only a few dozen PT algorithm calls.
To achieve accurate phase equilibrium calculations, full flash calculations are still necessary. The state-of-the-art trust-region method has demonstrated its superior robustness and efficiency in solving multiphase equilibrium problems (Petitfrere and Nichita, 2014; Pan et al., 2019). However, applying the conventional trust-region method to multiphase equilibrium calculations involving water, CO2, and heavy hydrocarbons may lead to convergence failures. To address this challenge, we develop a new modified trust-region method that incorporates two key enhancements: a scaled trust-region method, which ensures appropriate scaling of the Hessian matrix, and a non-monotonic trust-region method, which allows for greater flexibility in accepting trial points. Comparative analysis against the conventional trust-region algorithm shows that the modified trust-region-based algorithm effectively resolves non-convergence issues and improves computational efficiency. Lastly, considering that we are still lacking an algorithm for constructing four-phase envelopes, we develop a generalized algorithm that can be used to track four-phase boundaries. The algorithm can be also used to track three-phase boundaries. Testing it on six fluid mixtures demonstrates its superior robustness and efficiency in calculating both three- and four-phase boundaries
Exploring Pre-service Teachers’ Attitudes Toward Students with Disabilities of Differing Severity Levels
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Teachers’ attitudes toward students with disabilities are widely acknowledged as an important factor in the success of inclusive education. While substantial research has been conducted to examine teacher attitudes, there has been a notable scarcity of studies focusing on pre-service teachers. Pre-service teacher education is an optimal time for cultivating positive attitudes and fostering a commitment to inclusive education among future teachers. It is crucial to understand the attitudes of pre-service teachers toward students with disabilities in order to foster positive attitudes from the beginning of their
careers.
In this study, I examined pre-service teachers’ attitudes toward type of disability (academic, communication, and behavior) and severity level of disability (mild, moderate, and severe). The participants consisted of elementary (n = 313) and secondary pre-service teachers (n = 313) enrolled in the Bachelor of Education program at the University of Alberta. Participants completed the Attitudes, Beliefs, and Concerns about Teaching Students with Disabilities (ABCIES) survey. The current study examined a subset of 37 items from the ABCIES. This included participant responses to the Demographic items (n=10) and Behavioral Difficulties (n =9), Academic Difficulties (n=9) and Communication Difficulties (n=9). A mixed method ANOVA was conducted to examine the elementary and secondary pre-service teachers' attitudes towards students with academic, behavioural, and communication difficulties across the three severity levels. At each disability level (i.e., mild, moderate and severe), pre-service teachers reported the least favourable attitudes toward students with behaviour difficulties compared to academic and communication difficulties. There was no significant difference between academic and communication difficulties at the mild level.
Meanwhile, at the moderate and severe levels, pre-service teachers held more positive towards students with communication difficulties than academic difficulties. Additionally, secondary route pre-service teachers held more negative attitudes toward including students with academic, behaviour, and communication disabilities in general education classrooms. The findings of this study suggest that pre-service teachers’ attitudes are influenced by both the type and severity level of these three common disability categories. These findings highlight the need to foster more positive attitudes among pre-service teachers that may lead to more inclusive practices in their future classrooms, benefiting all students
Robust Forecasting-Aided State Estimation of Active Distribution Network with Multiple Distributed Generators
Get PDFThis paper develops a robust variational Bayesian unscented Kalman filter (RVBUKF) to track the state of active distribution networks with distributed energy resources. First, a spherical simplex-based unscented transform (UT) strategy is used to select Sigma points to reduce the computational burden. Subsequently, based on unscented Kalman filter and variational Bayesian theory, a variational update form of the covariance matrix is derived by using the inverse Wishart probability density function. To mitigate the impact of outliers, the Gaussian process regression model trained by historical data is used to
synchronously predict observation values to replace abnormal measurements. Finally, simulation experiments are conducted on the distribution system containing distributed generations (DGs). The numerical results demonstrate that the developed method can accurately track state changes in uncertain scenarios while reducing the computational burden of UT by almost half. In practical power systems, due to the randomness and fluctuation of distributed generation output, operating states of the active distribution network may frequently change, which places new demands on the dynamic state estimation
of active distribution networks. Therefore, in this paper, a new forecasting-aided state estimation method that combines robust Kalman filtering with machine learning techniques based on Gaussian process regression is proposed. The effectiveness of the proposed forecasting-aided state estimation was verified in the constructed active distribution network. Preliminary simulation experiments have shown that the number of Sigma points selected by SSUT is almost 50% of that of traditional UT, and the proposed method is capable of accurately tracking the dynamic changes of active distribution network states in various uncertain scenarios. In future research, we will address the problem of active distribution network forecasting-aided state estimation based on non-parametric modeling and attempt to use actual power grid data for testing
Language as Control: A Postcolonial Critique of Inner Mongolia’s Education Policies
Get PDFThis paper critically analyzes the recent educational language policy changes implemented by the Chinese government in Inner Mongolia, where ethnic Mongolians have historically maintained their distinct language and cultural heritage. Specifically, it examines the significant 2020 policy shift from Mongolian-medium instruction to Mandarin in core subjects. Using Postcolonial Theory as the central analytical framework, this research explores how such policies contribute to language loss, cultural assimilation, and the marginalization of Mongolian minority communities. The study highlights how Mandarin, while not historically a colonial language, operates similarly as a tool of linguistic imperialism, reflecting broader patterns of cultural hegemony. Drawing upon comparative insights from Canada's Indigenous language revitalization initiatives, the paper argues that successful language preservation requires community-driven bilingual education models, intergenerational transmission strategies, technological support, and robust legal frameworks for minority language protection
Insight into government, August 22, 2025
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