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The Okanagan
No full textThe Okanagan is a sculpture, drawing and installation-based thesis exhibition that uses the Early Sunrise peach as both a motif and material to explore the emotional impact of separation, distance, and the longing for connection. Through tactile and fragile forms, including felted peaches, porcelain peach pits, and charcoal stains, the works examine the complexities of intimacy, absence, and the passage of time. The unruly nature of the peach—its softness, messiness, and inevitable decay—mirrors the impermanent nature of emotional closeness. This exhibition presents a landscape that evokes the disintegration of intimacy and the rawness of yearning, inviting viewers to reflect on their experiences of presence, absence, and connection. Through the scale and materiality of the work, The Okanagan engages with the embodied nature of longing and the profound impact of separation
Using Artificial Intelligence for Some Activity Recognition and Anomaly Identification Using a Multi-Sensor Based Smart Home System
No full textAmbient Assisted Living (AAL) research frequently contends with limitations including
reliance on supervised data, lack of personalization and interpretability, and evaluations
in artificial laboratory settings. This study aimed to address these gaps by developing an
unsupervised, personalized, and interpretable AAL system using low-cost sensors for long-
term, real-world activity recognition and behavioural anomaly detection. A multi-modal
sensor network (including contact, vibration, outlet, air quality sensors) was deployed in a
single participant’s apartment for over 90 days. Primarily unsupervised machine learning
techniques, augmented with interpretability methods (SHAP), were used to identify key ac-
tivities (cooking, couch-sitting, showering) and detect personalized behavioural deviations.
Minimally supervised approaches for showering detection were also accurately achieved
using humidity data to address the shortcomings of unsupervised showering model. More
importantly, the system effectively identified interpretable anomalies demonstrating the
model’s capability to learn the individual’s normal behaviour in the home and identify
anomalies, representing significant deviations from the participant’s established routines.
In addition, the model was also able to be interpretable that allowed for the participant to
understand why each anomaly occured. This study confirms the feasibility of leveraging
unsupervised, interpretable methods with affordable sensors for personalized, ecologically
valid AAL, significantly reducing labelling dependence and enhancing system trustworthi-
ness for scalable, unobtrusive health monitoring
Game Plan for a Warmer World: Assessing the Climate Change Readiness of National-Level Canadian Sport Organizations
No full textClimate change is increasingly affecting sports, with warming temperatures and extreme weather events disrupting training and competition schedules, heightening health risks for athletes, coaches, and spectators (e.g., heat-related illnesses), as well as damaging sports infrastructure (e.g., flooded fields). At the same time, many sports and sports tourism are carbon intensive, prompting growing commitments to reduce emissions in line with the Paris Agreement. This study applies a structured content analysis, guided by an adapted climate policy integration (CPI) framework, to assess the climate change readiness of national-level Canadian sport organizations (n=86), including Sport Canada, Multisport Service Organizations (MSOs), and National Sport Organizations (NSOs). The integration of climate or environmental considerations into sport governance is critical for supporting the sector’s transition to low-carbon and climate-resilient operations. However, an analysis of official documents and websites found that the climate responses of national-level Canadian sport organizations are fragmented and insufficient, with 29.1% of organizations referencing climate change or environmental sustainability across any communication platform, 19.8% disclosing mitigation or adaptation initiatives, and only 3.5% showing alignment with international climate policy, such as the UN Sport for Climate Action Framework. It is argued that sport organizations must embed climate objectives into strategic planning, strengthen alignment with national climate policy, and build capacity for implementation. This transition should be supported by federal leadership, access to guidance and sector-specific resources, as well as international climate frameworks and best practices in sport sustainability
Understanding experiences of women’s empowerment through WASH/cash transfers toward post-COVID-19 recovery in Ghana
No full textcash transfers
water, sanitation, and hygiene
social norms
women's empowerment
water security
covid-1
Turning a New Leaf: Uncovering Medieval and Early Modern Roots of Canadian Forest Management for a More Sustainable Future
No full textCanada’s history of forest natural resource extraction and exploitation is often told through colonial perspectives of lumberjacks, river drives, and sawmills. Many historians neglect the influences of pre-industrial forest management that inform modern forestry practices in diverse ways. Of particular interest to the Canadian context are the multitude of management practices, techniques, and attitudes that can be found in the High Middle Ages of England. Coppicing, the cutting of a tree at the stump and subsequently harvesting the shoots after several years, practiced by the medieval English draws attention to principles of sustainability and ecological responsibility in a much more distant past. The Forest Charter of 1217 further asserts forests and access to its resources as a critical intersection between the physical and cultural environment of human societies. This thesis traces the evolution of forest management from these medieval legacies throughout the early modern period where global demands and social change initiate pivotal transformations in forest management in Europe and in the “New World,” leading to the devaluation of medieval practices. In doing so, this thesis identifies areas upon which historical perspectives inclusive of medieval legacies can address key issues regarding sustainability in modern Canadian forestry
Assessing ML classification algorithms and NLP techniques for depression detection: An experimental case study
No full text© 2025 Lorenzoni et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Context and background. Depression has affected millions of people worldwide and has become one of the most common mental disorders. Early mental disorder detection can reduce costs for public health agencies and prevent other major comorbidities. Additionally, the shortage of specialized personnel is very concerning since depression diagnosis is highly dependent on expert professionals and is time-consuming. Research problems. Recent research has evidenced that machine learning (ML) and natural language processing (NLP) tools and techniques have significantly benefited the diagnosis of depression. However, there are still several challenges in the assessment of depression detection approaches in which other conditions such as post-traumatic stress disorder (PTSD) are present. These challenges include assessing alternatives in terms of data cleaning and pre-processing techniques, feature selection, and appropriate ML classification algorithms. Purpose of the study. This paper tackles such an assessment based on a case study that compares different ML classifiers, specifically in terms of data cleaning and pre-processing, feature selection, parameter setting, and model choices. Methodology. The experimental case study is based on the Distress Analysis Interview Corpus - Wizard-of-Oz (DAIC-WOZ) dataset, which is designed to support the diagnosis of mental disorders such as depression, anxiety, and PTSD. Major findings. Besides the assessment of alternative techniques, we were able to build models with accuracy levels around 84% with Random Forest and XGBoost models, which is significantly higher than the results from the comparable literature which presented the level of accuracy of 72% from the SVM model. Conclusions. More comprehensive assessments of ML classification algorithms and NLP techniques for depression detection can advance the state of the art in terms of improved experimental settings and performance.Natural Sciences and Engineering Research Council of Canada (NSERC) || Centre for Community Mapping (COMAP)
A Biophysical Study on the Effects of Bacterial Infection and Neuroprotective Molecules in Relation to Alzheimer’s Disease
No full textAlzheimer’s disease (AD) is a prominent health concern among the aging population. This disease impairs neuronal cells, in part, through the accumulation of amyloid-β(1-42) peptide (Aβ1-42) and its various toxic mechanisms. With the advent of controversial anti-amyloid drugs, the efficacy of newly developed treatments is still not high. Investigations into novel treatment strategies highlight the potential protective abilities of natural products; one of which is melatonin, a hormone produced by the pineal gland. Due to its inherent lipophilicity and interaction with membranes, melatonin is investigated in this work as a novel membrane-protection strategy. The difficulty in discovering effective anti-amyloid drug targets may be related to Aβ1-42’s physiological role as an antimicrobial peptide. Several hypotheses suggest microbial infections as a causative risk factor of AD through the stimulation of Aβ1-42 and neuroinflammation. This work specifically focuses on the contributions of bacterial functional amyloids, known as ‘curli fibers’, to Aβ1-42 processes. The basis for this investigation into curli fiber-amyloid interactions lies in multiple established instances of cross-seeding with other amyloidogenic peptides. Recently published studies demonstrate this interaction but many questions still remain as to the nature and effect that this interaction will have on other AD processes.
In this work, we intend 1) to elucidate the molecular mechanism of melatonin membrane protection against amyloid toxicity and 2) to investigate the interaction of infection-establishing bacterial curli fibers with Aβ1-42 and the effect of these complexes on AD-associated mechanisms. To explore these mechanisms, we used multiple methods in biophysics, molecular biology, and computational chemistry to provide an interdisciplinary perspective. Previously published lipid models mimicking various AD-afflicted neuronal membranes were pre-treated with melatonin prior to Aβ1-42 and resulting damage was assessed via atomic force microscopy (AFM) imaging and black lipid membrane electrophysiology. In this work we demonstrated melatonin’s ability to inhibit peptide binding and promote membrane repair after Aβ1-42 exposure is dependent on its fluidic nature working in combination with lipid composition of target membranes. Similarly, high speed-AFM, AFM, and BLM studies evaluated the differences in antimicrobial and toxic mechanisms of Aβ1-42 in comparison to a known antimicrobial peptide. These evaluations revealed that anionic bacterial membranes repel Aβ1-42 indicating antimicrobial activity is not likely related to the same non-specific membrane binding as is its toxicity.
Next, curli-amyloid interactions and the identification of participating aggregation states were evaluated through molecular dynamics simulations, transmission electron microscopy and AFM, as well as a novel biomolecular condensate assay. We confirmed that not only do curli fibers and Aβ1-42 interact and form peptide complexes, but also identified that this interaction is solely carried out by early aggregation species such as monomers and oligomers. Additionally, the effects of curli fibers on Aβ1-42 toxicity were first modelled by MD simulations and experimentally confirmed through measurements of damage on simple eukaryotic-based models by AFM and BLM, cell viability assays of murine microglial cell cultures, and immunogenicity evaluations using enzyme-linked immunosorbent assays. We also established that these curli-amyloid complexes reduce toxicity directly related to membrane perforation mechanisms but still negatively affect cell viability, presumably due to a heightened immunogenicity of the peptide complexes.
Our findings lead us to propose a novel membrane-centric neuroprotection strategy against Aβ1-42 toxicity. This proposed mechanism intends to expand our knowledge of melatonin’s effect on membranes in AD and could inform therapeutic development. Furthermore, our investigations into curli-amyloid interactions and their effect on AD processes highlights the important role of Aβ1-42 in physiology and how this can relate to AD onset. These findings can reinforce the current research paradigm shift to microbial infections, the gut-brain axis, and the role of microbial products as potent initiators of AD onset pathways. Therefore, this entire body of work aims to develop knowledge of important AD mechanisms to guide new research, diagnostics, and treatment avenues
Discriminating and Localizing Thermal Aging in Low Voltage Polymeric Cables using Non-Destructive Electrical Diagnostics
No full textCurrently, in North America and Europe, significant attention is being paid to the condition of LV electrical cable systems in nuclear power plants (NPPs), given industry and governmental efforts to extend the operational life of numerous existing NPPs well beyond their initial 40-year design life / license period. Thermal degradation is a key cause of long-term aging of LV NPP polymeric cable insulation materials in normally dry areas, and in-situ condition monitoring techniques form an important means of diagnosing its deleterious effects within the scope of a LV cable aging management program. However, key knowledge gaps have thus far precluded the development and deployment of quantitative assessment criteria or predictive modelling/classification methods which can be practically applied towards in-situ, electrically-based diagnostics of thermally aged LV NPP cables across varying insulation types, environmental conditions, rates of dielectric aging, and aging volumes/damage ratios.
This thesis investigates fundamental and practical aspects of applying a combined electrical diagnostic approach using DC time-domain, low frequency (0.001 – 1000 Hz) AC, and travelling wave (RF) based in-situ terminal electrical diagnostic techniques to discriminate and localize thermal aging in LV NPP cables. A unique custom experimental set-up is designed and constructed to subject 197 XLPE and EPR prepared LV shielded cable test specimens (prepared from full-scale LV NPP cables) to accelerated thermal aging, whilst simultaneously considering the influence of varying, field-representative experimental conditions. Representative diagnostic data based on approximately 2120 low frequency dielectric spectroscopy (LFDS)/polarization depolarization current (PDC) and 1175 frequency domain reflectometry (FDR) measurements on these test specimens are subjected to varying forms of data analytics including empirical analysis of full spectra, partial least squares regression based predictive modelling, supervised ensemble decision tree-based (random forest) classification and predictor importance analysis.
Experimental results from AC/DC (LFDS, PDC-based) electrical diagnostic tests, reference physical-chemical material characterization tests and related data analytics illustrate that material and dielectric changes due to accelerated thermal aging can be discriminated using LFDS and PDC-based electrical diagnostics in combination with interpretable empirical, statistical or supervised ML-based techniques. It is however important to derive a suitably wide range of physically meaningful potential predictor metrics based on the underlying full spectrum dielectric data, including real permittivity (ε′), imaginary permittivity (ε″), polarization current and depolarization current. It is also imperative to undertake all analyses on an insulation-specific basis for LV NPP cables given differences in polymer formulations and physical-chemical evolutions with accelerated thermal aging, and undertake quantitative, insulation-specific approaches to interpret which predictors bear higher responsibilities for the observed dielectric data variance and trends (with respect to thermal aging). Experimental results from RF (FDR-based) electrical diagnostic tests and related data analytics show that thermal aging can be localized using differential, inverse fast Fourier transform (IFFT) converted S11 responses, in combination with advanced window-based signal processing methods and interpretable, supervised ML-based binary anomaly classification techniques. The research supports the premise that FDR measurements should be utilized for defect localization (ideally based on long-term monitoring/trending) rather than condition assessment, especially for samples in early stages of thermal degradation (e.g., antioxidant depletion, start of mechanical hardening) as present in this research.
The research outcomes highlight the importance of utilizing an experimental design and broad population dataset for LV NPP cable aging management research which at minimum, considers field-representative variations in thermal aging rates, thermal aging volume/damage ratio, thermal aging locations, measurement (ambient temperatures), and insulation type. Adopting such an approach in this work has allowed for a robust, insulation-specific assessment of critical diagnostic predictors for thermal aging discrimination and localization, and proof-of-concept development and validation of predictive/classification-based models which can yield conservative performance assessments for new (i.e., previously unseen) diagnostic data obtained from field-representative populations. The research undertaken can thus be considered a key knowledge development step to facilitate the deployment of AC, DC and RF electrical non-destructive examination (NDE) techniques towards the aging management of thermally degraded LV cables installed in NPPs or other critical environments
The use and impact of virtual reality programs supported by aromatherapy for older adults: A scoping review protocol
No full text© 2025 Hung et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Both virtual reality and aromatherapy have shown promising impacts on the health and well-being of older adults. Aromatherapy has been reported to enhance immersive experiences during virtual reality programs. However, studies on the combined use and impact of virtual reality and aromatherapy for older adults have not been systematically reviewed. Therefore, this scoping review will identify existing types of virtual reality programs supported by various forms of aromatherapy and their outcome measures and results on the well-being of older adults. This review will be conducted in accordance with the Joanna Briggs Institute methodology or scoping reviews and will be reported in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews. The search strategy will encompass both published and unpublished papers. The databases to be searched are CINAHL, MEDLINE, Embase, Scopus, Web of Science, ACM digital library, IEEE Xplore digital library, Compendex, ProQuest, and Google Scholar. Two independent reviewers will perform title and abstract screening, full-text screening, and data extraction. Data analysis and synthesis will be discussed by the whole research team, mapped in the literature table and accompanied by a narrative summary. Scoping review data will be collected from publicly available articles; research ethics approval is not required These findings will be disseminated through a peer-reviewed publication and conference presentations.Brain Canada in collaboration with Alzheimer Society of Canada, GR028233
Effect of Feedstock Powder, Processing Parameters, and Post-Heat Treatment on Cold-Sprayed Cu Alloys: Development of Heterogeneous Material
No full textCold spray (CS) is a solid-state powder deposition technique that utilizes high-velocity impact to bond particles onto a substrate or previously deposited layers in a layer-by-layer approach. This technology is extensively used for repairing high-performance components, protective and functional coatings, and 3D printing applications. However, a major challenge in cold spray additive manufacturing (CSAM) is understanding the relationship between feedstock powder characteristics and the final deposit properties. This thesis focuses on characterizing feedstock powders and analyzing the microstructure, physical properties, and mechanical behavior of cold-sprayed deposits. A key limitation in CS deposited materials is their low ductility. To address this, various strategies, including process optimization and post-deposition heat treatment, are explored to enhance their mechanical properties. Furthermore, a novel approach has been introduced to improve ductility through the fabrication of heterogeneous laminated structures, where alternating layers of hard and soft alloys create a microstructure with fine-grained and coarse-grained regions, ultimately enhancing the mechanical performance of CS deposits.
To explore this, the cold-sprayability of various Cu powders produced by electrolysis, gas atomization, and grinding was examined and compared using scanning electron microscopy (SEM), electron backscatter diffraction (EBSD), particle analysis (CAMSIZER, FT4 powder flow tester), and nano-hardness testing. The results indicated that the spherical morphology of gas-atomized powders had a lower surface area compared to the irregular-shaped electrolytic and ground powders, reducing surface interactions and improving powder flowability. Additionally, gas-atomized Cu powders with equiaxed grains exhibited an average nano-hardness value, balancing flowability and deformability. Therefore, these powders were identified as promising feedstock materials for CS applications. Furthermore, the impact of these powders on coating microstructure and mechanical properties was investigated. A comprehensive statistical model was developed to optimize process gas temperature and pressure for deposition. It was found that operating near the upper temperature and pressure limits of the low-pressure cold spray (LPCS) system resulted in coatings with minimal porosity, high flattening ratio, increased microhardness, and enhanced bonding strength. Surface and microstructural evolution analysis revealed that lower oxide content near the surface of more spherical, satellite-free powders significantly enhanced plastic deformation and grain refinement during deposition.
Improving the mechanical properties of CS depositions for optimal strength and ductility under uniaxial tensile testing leads to the next step, which involves investigating the effects of different processing gases (Nitrogen and Helium) and post-heat treatment on pure Cu. The findings suggest that while Cu 3D-printed parts processed with He exhibit higher deposition efficiency, N₂-processed samples show greater plastic deformation and lower porosity due to the higher number of deposition passes and the peening effect required to achieve the same thickness as He-processed samples. Heat treatment, when applied at an appropriate temperature and duration, enhances interfacial bonding, promotes recrystallization, and facilitates grain growth, leading to strength and ductility improvements of up to 2.7 times and 28 times, respectively. Heat treatment also plays a critical role in defining the microstructure and mechanical performance of CSAM CuCrZr alloys, an area that remains less explored. The as-sprayed CuCrZr alloy exhibited weakly bonded particle interfaces and porosity, which were significantly reduced by solution annealing and age hardening (SA+AH). This process led to grain reorganization, interfacial healing, solid-state diffusion bonding and precipitation of ultra fine particles resulting in strength and ductility enhancements of up to 2.4 times and 9 times, respectively.
Engineering heterogeneous microstructures has emerged as an effective strategy to enhance the mechanical behavior of materials processed through various thermomechanical and manufacturing techniques. However, this approach remains largely unexplored in 3D-printed cold-sprayed components. In this study, a dual heterogeneous laminated Cu/CuCrZr composite structure with varying interface spacing was developed using LPCS followed by post-heat treatment. This tailored microstructure consists of alternating coarse- and fine-grained regions, generating microstructural contrast that induces hetero-deformation-induced (HDI) strengthening. The mechanical incompatibility between the soft Cu and hard Cu-Cr-Zr layers enhances strength and ductility, with decreasing interface spacing improving strength and ductility by up to 10% and 28%, respectively. To further understand the strain hardening mechanisms, loading-unloading-reloading (LUR) experiments and microstructural
analyses were conducted. The findings attribute the enhanced mechanical performance to well-bonded particles and HDI strengthening, driven by geometrically necessary dislocations (GNDs) at heterogeneous interfaces. This effect improves work-hardening capacity, leading to simultaneous increases in both strength and tensile ductility of the laminated alloy. While this innovative heterogeneous design strategy for cold-sprayed materials requires further exploration across various topologies, heat treatment methods, and alloy systems, it presents a promising approach to enhancing strength and ductility in low-pressure cold spray materials