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Optimization of tall buildings subjected to wind load using genetic algorithm and image-based machine learning
No full textAs the prevalence of tall buildings are on an upward trend in urban cities, the need to navigate their design intricacies becomes increasingly important. Tall buildings exhibit dynamic and nonlinear responses to applied load and are required to satisfy extensive design requirements, leading to the need for complex analysis techniques to evaluate very specific engineering problems. Technological advances in supporting research fields provide engineers with both computational resources and algorithms for use as tools not previously available, strengthening the case for Machine Learning (ML) and surrogate modelling techniques to assist with the interpretation and exploration of design spaces in advanced analysis. This thesis studies the use of Convolutional Neural Networks (CNNs) designed to simultaneously facilitate the optimization of Reinforced Concrete (RC) shear wall topology and size. As an image-based approach, the work assesses the capability of the proposed algorithms to generalize the abstract relationship between structural layout and numerical performance metrics of tall building designs. The resulting models display significant capability of replicating Finite Element Analysis (FEA) corresponding to structural layout images. Further, an optimization framework is developed which utilizes these models and a Genetic Algorithm (GA) to automate the design processes which typically rely on the expertise of engineers. As a result, both architects and engineers can utilize the proposed framework to identify design solutions corresponding to a reduced quantity of shear wall volume, and/or total number of piers required, achieving cost savings in real-world applications. This work reveals the potential of CNN-based surrogate models in the design of tall buildings, especially when proposed for structural and multidisciplinary optimization.
KEYWORDS
Tall buildings, wind load, shear wall, Reinforced Concrete (RC), Latin Hypercube Sampling, Convolutional Neural Network, Genetic Algorithm (GA), multi-objective optimization, wind engineering, surrogate model, deep learning, performance-based design, Computational Fluid Dynamics (CFD), structural optimization, Design Space Exploration (DSE), Finite Element Analysis (FEA)
JoyPopTM as a complementary tool to treatment
No full textGiven the growing rates of mental health concerns and the need for accessible support tools among youth (ages 12-24), mobile health (mHealth) apps offer an avenue to support youth. Little research has determined where mHealth apps can be implemented as complementary tools alongside traditional mental healthcare, including youth and staff perspectives and support recommendations in utilizing such tools. One mHealth app, JoyPopTM, is an evidence-based tool that supports youth emotion regulation and coping skill development. The current study assessed the utility of the JoyPopTM app as a complementary support tool that may help to promote mental health and wellness outcomes among youth clients (ages 12-18) receiving services at a local children’s mental health organization in Northwestern Ontario (Children’s Centre Thunder Bay). The study used an implementation science approach via the RE-AIM (reach, effectiveness, adoption, implementation, and maintenance) framework to support the transferability of study findings and inform the implementation of similar mHealth tools. This as done through the combination of qualitative and quantitative methodologies in line with the frameworks’ dimensions to answer the following research questions: 1) how adding the JoyPopTM app affects youth treatment outcomes across four months (effectiveness), 2) to what extent youth use the JoyPopTM app alongside services and reasoning for trends (reach, implementation), 3) to what extent staff support using the JoyPopTM app as a tool throughout services and reasons for these trends (adoption, implementation) 4) youth client perspectives on maintaining JoyPopTM app use alongside treatment (maintenance), and 5) staff perspectives of maintaining their support of JoyPopTM app use alongside services (maintenance). Specifically, youth participants (n = 40, 60% female; Mage = 15.03, SD = 1.79) were given access to the JoyPopTM app at the beginning of their treatment and completed self-report assessments of their therapeutic progress in comparison to a subset of youth clients who did not receive the JoyPopTM app (n = 59, 64% female; Mage = 15.03, SD = 2.12) using a routine outcome monitoring measure (Partners for Change Outcome Management System, PCOMS). Youth and staff completed semi-structured interviews of their JoyPopTM app use and integration, respectively, after one and four months. Linear mixed modelling and interview findings were used to assess research question one and effectiveness. Descriptive analyses, back-end app use data, and interview findings were used to assess research questions two and three, as well as reach, adoption, and implementation. Research questions four and five were assessed using interview data. Interview categories were derived via deductive and inductive content analysis. Regarding research question one and effectiveness, results indicated that while both JoyPopTM and the non-user comparison group showed significant improvements in their PCOMS scores over time, they did not significantly differ from each other in this improvement. Additionally, most youth and staff perceived the app to have a positive impact on youth treatment (e.g., improving mood, adjunct tool). For research question two regarding reach and youth implementations, results indicated the success of the study’s reach via recruitment strategies. Additionally, successful youth implementation of the app was primarily outside of sessions, with facilitators (e.g., being easy to use) and barriers (e.g., forgetting to use). Similarly for research question three, staff support and adoption of the app were moderately successful based on recruitment support. Additionally, implementation success outside of sessions was moderate, with facilitators (e.g., JoyPopTM coordinator role) and barriers (e.g., busy sessions and organization expectations). For research questions four and five, results indicated successful maintenance of continued app use and support with consideration of youth contingencies and recommendations (e.g., as a calming tool, with feature improvements or additions), as well as staff ones (e.g., if they know more about the app, with youth accessibility considerations and more training), to support ongoing use. Overall, results supported the relative success of the JoyPopTM app as a clinical tool through its consideration of the RE-AIM dimensions, with important insight into the necessary conditions to support uptake and use of a youth-oriented mHealth app alongside mental healthcare services for youth. Findings emphasized the important role that mHealth tools can play as adjunct resources to support youth wellness outcomes and the necessary parameters for their successful integration among youth clients and staff. Notably, digital tools may require ongoing internal support within the organization (e.g., app coordinator, management support). Youth and staff recommendations can also inform app development to enhance JoyPopTM’s potential to support youth service delivery and mental health outcomes among other future organizations.
Keywords: JoyPopTM, Youth, mHealth, RE-AIM, Community-Based Researc
The influence of ethanol consumption on physiological and perceptual responses and postural control during acute heat exposure in older adults
Full text linkThe family picture: A collection of family case studies exploring roles, relationships, and identities after a dementia diagnosis and a transition into a long-term care home
Full text linkObjectives:
This study aimed to explore people living with dementia and family members’ meaning of family after a transition into a long-term care home and the construct of family at the family and individual levels. Additionally, this study aimed to explore changes in roles, relationships, and identities within and between families along their dementia journeys.
Methods:
A narrative case study approach was used to present the family stories of each of the four cases featured. The method of analysis used was voice-centred relational analysis (VCRA) which included four readings of the data. The combination of narrative case study approach and VCRA provided a solid foundation to build upon with the sensitizing concepts of roles, relationships, and identities. The narratives and findings utilized quotations from people living with dementia and family members to analyze the sensitizing concepts.
Findings:
From the findings, four main themes emerged, including the meaning of family, maintaining identity, dementia as a disruptor, and dementia as a transformer. Family meant connection, safety, and love for many of the families featured in this study. Maintaining the identities of people living with dementia through roles, music, and fashion helped maintain their autonomy and dignity. Finally, these themes explored dementia as a disruptor of participants’ lives and a transformer of roles, relationships, and identities.
Conclusion: All of the families featured in this study experienced disruptions and transformations of roles, relationships, and identities after a family member living with dementia moved into a long-term care home. Both positive and negative changes were described, which illustrated that dementia was not always a disruptor towards the negative, but a disruptor toward something new and transformed. This study emphasized the complexity and importance of family relationships
Advancing object detection models: an investigation focused on small object detection in complex scenes
Full text linkSmall object detection remains a persistent challenge in computer vision, especially in safetycritical
applications, such as autonomous driving and aerial surveillance, where objects of interest
often occupy only a few pixels and are easily lost in cluttered scenes. To advance the performance
of small object detection models, this thesis proposes two novel approaches focused on increasing
both accuracy and robustness.
The first approach introduces a semantic segmentation-guided feature fusion framework, where
contextual cues from a segmentation model are integrated into the object detection pipeline. A
lightweight attention mechanism is used to merge semantic and visual features, enhancing the
detection of small objects. The experimental results demonstrate clear improvements in identifying
challenging small targets, proving the effectiveness of cross-task feature integration.
The second approach utilizes feature pyramidal structures to improve multi-scale feature representation
through a novel dilated strip-wise spatial feature pyramid, which employs dilated stripwise
depth convolutions. Evaluated on the VisDrone and AI-TOD benchmark datasets, this model
shows significant improvements over the baseline, effectively detecting objects in densely packed
environments. The approach achieves state-of-the-art performance on the AI-TOD dataset.
Together, these approaches offer distinct strategies for overcoming the limitations of the existing
object detection models. The research findings emphasize the importance of both semantic
guidance and spatial feature refinement in enhancing small object detection
Listening to Animbiigoo Zaagi’igan Anishinaabek
Full text linkTraditional foods are integral to the Animbiigoo Zaagi’igan Anishinaabek (AZA) First Nation in Northwestern Ontario. More than simply sustenance, they also play an integral role in the community’s culture, health and well-being, knowledges, and teachings. The impacts of environmental contaminants on their territories, located near Jellicoe, Ontario, are a significant concern for community members. While much of the scientific research has deemed these contaminants safe, many Elders, Knowledge Keepers, hunters, gatherers, and youth have expressed negative impacts on their food systems. This research aimed to explore the experiences and perspectives of AZA First Nation members regarding the impacts of environmental contaminants on food self-determination. It is based on a collaborative project between AZA First Nation, Understanding Our Food Systems, the Thunder Bay District Health Unit, and Lakehead University. Together, we explored the impacts of glyphosate spraying, a non-selective herbicide used to control unwanted vegetation, on AZA First Nation’s lands, peoples, and non-human kin and what can be learned using different ways of knowing to advance healthy communities and environments. This research adds to existing conversations on glyphosate's impacts on human and environmental health and well-being and contributes to ongoing Indigenous food sovereignty work in the community and across the region. It also aims to add to the existing literature on using different ways of knowing when addressing complex issues
Functionalized lignin derivatives for flame retardant, thermal insulation, and coating applications
No full textLignin, a highly branched and heterogeneous biopolymer composed of phenolic units, is a key structural component of plant cell walls, providing mechanical strength, water resistance, and protection against microbial degradation. Due to its complex chemical structure and functional versatility, lignin has gained significant attention for its potential applications in biomaterials, emulsifiers, and sustainable industrial processes. Chitosan, a biopolymer derived from chitin, is widely recognized for its biocompatibility, biodegradability, and antimicrobial properties, making it a valuable material for various industrial and biomedical applications. When combined with lignin, the resulting composite benefits from enhanced structural stability, improved functional properties, and synergistic effects, offering promising potential for sustainable biomaterials, emulsifiers, and advanced functional coatings. In this thesis, functionalized lignin was produced following different pathways and used as an emulsifier for oil-water emulsions and advanced coating applications. It was also combined with chitosan to develop multifunctional materials with enhanced thermal insulation properties.
The first experimental part of this thesis presents a method for producing a phosphorylated lignin-derived (PK) flame retardant following a solvent-free polycondensation reaction of kraft lignin (KL) and phytic acid (PHA). The reaction was optimized for low temperature and high decomposition temperature. Advanced techniques confirmed the covalent bonding between PHA and KL oxygen, resulting in high decomposition temperature and char formation. The study provided a new approach for preparing a fully bio-based flame retardant with limited smoke density and higher limiting oxygen index, following a green chemistry concept.
The second experimental chapter utilized optimized reaction conditions to produce lightweight, thermally insulated, and flame-retardant aerogels (APK) from chitosan (CH) and phosphorylated kraft lignin (PK). The production process improved porosity reduced thermal conductivity, and enhanced compression strength, making APK an excellent thermal insulator for construction.
The third experimental part of this thesis explores the production of sustainable aerogels from carboxymethylated lignin (CM), a biodegradable material with a lower environmental footprint. The research found that increasing the charge density of CM intensified the crosslinking bond between CM and chitosan (CH), reducing porosity and compression strength, and increasing thermal conductivity. The least charged CM (CM1) aerogel had the least thermal conductivity and the highest compression strength. The results of this chapter suggest a promising strategy for creating eco-friendly, sustainable aerogels.
The fourth experimental section of this thesis investigated the interaction between lignin derivatives and oil and water in emulsion systems. The reaction condition for the charge density of -1.5 mmol/g was optimized with Taguchi for both sulfoethylation (SL) and carboxyethylation (CL) of kraft lignin (KL). These modifications were found to generate functional emulsifiers for soybean emulsions at different pH levels, such as 3, 7, and 11. The study found that SL and CL produced Pickering emulsions with oil droplet sizes of 436 and 452 nm at acidic pH but had shorter lifespans under acidic conditions. The study also found that SL had higher elasticity and interaction at pH 11, highlighting the importance of lignin upgrading techniques in generating functional emulsifiers.
Overall, this thesis advances the development of multifunctional materials by combining functionalized lignin with chitosan, resulting in enhanced thermal insulation and flame retardancy. The research introduces innovative methods for producing bio-based flame retardants, lightweight aerogels, and sustainable emulsifiers, highlighting the potential of lignin in various industrial applications. The findings contribute to the creation of environmentally friendly, high-performance materials with improved structural stability and functional properties
Performance assessments in mathematics education
Full text linkThis qualitative case study explores elementary teachers’ perceptions and practices
regarding performance assessments in mathematics education. Grounded in a social
constructivist framework, the research investigates how teachers believe performance tasks
impact students’ understanding of mathematical concepts and the characteristics of these
assessments as implemented in classrooms. The study aims to provide an in-depth understanding
of how performance assessments are used to foster meaningful learning and problem-solving
skills, addressing the gap in literature about practical applications of these tools in real-world
settings.
Three elementary teachers, selected through purposive sampling, participated in semistructured
virtual interviews. Data collection involved detailed interviews to capture teachers’
experiences, thoughts, and strategies for using performance assessments. The data were analyzed
through open coding, thematic analysis, and the use of NVivo software to identify recurring
themes and insights.
Findings highlight the value of performance assessments in promoting student-centered
learning, critical thinking, and collaborative problem-solving. Teachers reported challenges in
transitioning from traditional paper-pencil tests to authentic assessments, emphasizing the need
for professional development and support. This research contributes to understanding how
performance assessments can enhance teaching practices, align with curriculum goals, and foster
deeper student engagement in mathematics education
Examining the contribution of demands and resources in the development of burnout among post-secondary students training for careers in health care
No full textPost-secondary students who are exposed to chronic stress, emotional exhaustion, and academic demands, and who do not have enough resources to help them cope with and recover from stress can develop symptoms of burnout. Burnout is a serious problem for students because it can increase drop out intentions and can negatively impact their physical and mental health. Students training to work in health care may experience practicum-related burnout or academic burnout, due to their multiple demands. Certain types of resources, however, can moderate the relationship between the demands these students face and the progression of burnout. A gap exists in the literature examining the academic and placement related resources and demands and their relationship to burnout in students training for health care careers. Using the Job Demands and Resources theory, the present study examined which combination of demands and resources predicted academic and placement-related burnout. Through moderation regression analyses we found that compassion satisfaction and university resources moderated the relationship between university demands, secondary traumatic stress, effort and reward imbalance and placement related burnout, rather than academic burnout. This study could help future studies in the development of programs working to provide support to health care students to help reduce their levels of burnout before they enter the workforce.
Keywords: health care students, burnout, vicarious trauma, secondary traumatic stress, Job Demands and Resources Theor
Deep learning in dermatopathology: applications for skin disease diagnosis and classification
Full text linkMedical image segmentation is pivotal in disease diagnosis and treatment planning across various imaging modalities, including MRI, CT, ultrasound, X-ray, dermoscopy, and histopathology. This systematic literature review, conducted using the PRISMA framework, provides a comprehensive analysis of Deep Learning approaches applied to medical image segmentation, with a focus on dermato-pathology for skin disease diagnosis and classification. Transformer-based models have shown notable improvements over traditional CNN architectures, achieving up to 79.95% accuracy in multitask cancer detection tasks, surpassing CNN-based models that achieved 74.05%. In liver lesion segmentation using CT scans, attention-enhanced U-Net models achieved a 93.4% Dice Similarity Coefficient (DSC) for liver tissue and 77.8% for tumor segmentation. In dermoscopy, self-supervised transformer-based models like G2LL exceeded 80% accuracy, while U-Net-based models for skin lesion segmentation achieved up to 93.32% accuracy. Histopathology image analysis further demonstrated that models incorporating attention mechanisms, such as the PistoSeg framework, improved segmentation precision by up to 7.15% compared to conventional methods. Across various modalities, Deep Learning models consistently outperform traditional methods, with improvements ranging from 5 to 15% in accuracy and segmentation metrics. Despite challenges such as computational demands and the need for large annotated datasets, Deep Learning continues to revolutionize medical image segmentation, offering higher diagnostic precision and outlining future research directions to bridge existing gaps