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Forgiving childhood adversities: exploring mental health benefits and pathways of influence
(2026) Onno, Karin A.; Mushquash, Aislin; Maranzan, Amanda; Krykylwy, James
The term “childhood adversity” encompasses negative experiences such as abuse, neglect, and household dysfunction. These experiences are associated with difficulties in adulthood, including psychological distress. Evidence suggests that the negative impacts of childhood adversity may be attenuated if individuals had some positive experience during childhood, have resilience characteristics, or learn to increase their reliance on adaptive coping strategies and reduce their reliance on maladaptive coping strategies. Forgiveness is a coping response that may reduce the negative impacts of adversity. Researchers vary in their conceptualizations of forgiveness. While forgiveness is generally associated with more favourable outcomes, very few studies have examined the benefits of forgiveness for adults with childhood adversity. The present research examined the associations between (a) childhood adversity, (b) forgiveness of others, self, and situations related to individuals’ childhood adversity, and (c) psychological distress. In this study, the trait- vs. state-forgiveness distinction was adopted—while the former refers to the tendency to forgive across time and situations, the latter refers to forgiveness related to a specific transgression. Forgiveness was organized by “target” to examine forgiveness of others, self, and situations. Mediation and moderation models were estimated and compared to assess the extent to which forgiveness of childhood adversities explains or influences the relationship between childhood adversity and psychological distress. The present study found that the estimated mediation model better explained the relationships between childhood adversity, forgiveness, and psychological distress than the estimated moderation model. This suggests that childhood adversity may undermine ones’ ability to forgive their childhood adversities, which in turn may contribute to increased day-to-day psychological distress in adulthood. Given that the moderation model was non-significant, it may be more important to further understand the causal pathway in which childhood adversity undermines individuals’ forgiveness towards targets associated with their childhood, rather than solely focusing on promoting forgiveness within this population.
Rosies of the North: women’s employment at Canadian Car and Foundry in Fort William during the Second World War
(2026) Henits, Shylin; Beaulieu, Michel S.
“Rosies of the North: Women’s Employment at Canadian Car and Foundry in Fort William during the Second World War” is an analysis of women’s employment at the Canadian Car and Foundry (Can Car) plant in Fort William, Ontario, during the Second World War, focusing on the experiences of the “Rosies of the North” who built military aircraft for the Allied war effort. Drawing on employee records, plant newspapers, union publications, oral histories, and wartime media, it situates women’s industrial labour within the broader economic and social context of the Great Depression, wartime mobilization, and post-war demobilization. The study analyzes women’s recruitment, training, working conditions, union involvement, and participation in home front initiatives, alongside persistent gendered divisions of labour, unequal pay, and supervisory practices.
Development of stochastic models for streamflow synthesis based on colored-noise–dominated hydrologic systems: textural pattern modelling for black- and pink-noise systems and Brownian Motion modelling for brown-noise systems
(2026) Studnicka, Shirin; Panu, Umed; Li, Deli; Cui, Liang; Bajwa, Garima; Singh, Vijay
Civil engineering structures are often designed based on flood design criteria; however, there is no guarantee that these design floods represent worst-case scenarios. It is because streamflow has been recorded for only a few hundred years, while rivers have been running for over the past thousands of years. Consequently, the available records may not include the most extreme flood events. This limitation creates a need for synthesizing additional possible streamflow scenarios. Over the decades, hydrologists have developed various methods to synthesize streamflow, ranging from early stochastic models in the 1960s to AI-based approaches of today. The evolution of streamflow synthesis methods relies on two equally important considerations: a deeper understanding of streamflow characteristics and advances in computational techniques. In some cases, however, the first consideration is overlooked under the assumption that more complex models can better synthesize possible scenarios. Therefore, this research focuses equally on these two crucial considerations: (1) improving the understanding of monthly streamflow characteristics, and (2) developing models according to the identified characteristics. To improve the understanding of streamflow characteristics, this research quantifies scaling behaviour as a representation of the memory of hydrological systems using an approach based on identifying jumps in the power spectral density of the system being analyzed. Using this framework, 143 hydrometric stations in Ontario were analyzed to identify pink-, brown-, and black-noise–dominated monthly streamflow behaviour. Of these, two stations were characterized by the dominance of brown noise, 39 by pink noise, and the remaining 102 by black noise. Based on the identified dominant noise, the development of modelling approaches is then initiated in this research. For hydrometric stations dominated by the brown noise, an Extended Geometric Brownian Motion (EGBM) model has been developed. Compared with a multiplicative ARIMA model, the EGBM better reproduces the statistical properties of historical streamflow and yields residuals with statistically insignificant lag-1 autocorrelation. For stations exhibiting pink- or black-noise dominance, a textural pattern recognition system (TPRS) has been developed. The comparison of TPRS and ARIMA/SARIMA models shows that for pink-noise–dominated watersheds, although TPRS performs slightly better, the improvement is not sufficient to justify the added model complexity in cases where high accuracy gains are not required. In contrast, black-noise–dominated watersheds exhibit stronger temporal dependence, which is more appropriately captured using the proposed TPRS framework. Further comparison of the proposed TPRS with traditional pattern recognition methods demonstrates its effectiveness in reproducing autocorrelation structures and higher-order statistical features. The results show that the TPRS improves the representation of the autocorrelation function (ACF) up to 100 lags, compared with 24 lags for traditional pattern recognition methods. Moreover, the Hurst coefficient analysis confirms that the TPRS model provides a slightly enhanced representation of statistical characteristics exhibited by the historical time series. These findings support more informed model selection by reducing the mismatch between model structure and underlying hydrological behaviour, ultimately improving the reliability of synthesized streamflow scenarios for flood frequency analysis and flood risk assessment.
Sustainable, safe, smart, and connected building management to reduce greenhouse gas emission
(2026) Goonetilleke, Vinuri Nilanika; Heenkenda, Muditha; Zaniewski, Kamil; Cornwell, Adam; Malik, Karim
Climate change is a critical global challenge, and the building sector accounts for nearly 30% of global greenhouse gas (GHG) emissions, remaining a key target for mitigation. Indoor environments contribute significantly to GHG emissions, primarily through heating, cooling, lighting, and occupant-driven energy use. Indoor mapping, serving as the foundation for Digital Twins (DTs), provides a spatiotemporal framework that integrates sensor data with Building Information Modelling (BIM), Geographic Information Systems (GIS), and Internet of Things (IoT) to support energy-efficient, low-carbon building operations. However, many small- to medium-sized organizations lack practical frameworks to integrate indoor spatial data with operational and energy management systems. Therefore, this study aimed to develop an integrated solution (a DT-based indoor mapping system) to support sustainable and smart building management practices for the office space of Four Rivers Environmental Service Group in Thunder Bay, Ontario. Architectural plans, occupancy records, and energy data were combined with newly acquired spatial information from the laser distance meter and Light Detection And Ranging (LiDAR) to generate accurate 2D CAD drawings and 3D building models. These datasets were imported into ArcGIS Pro software and processed using ArcGIS Indoors tools to create a comprehensive indoor mapping environment that incorporates spatial features, room geometries, and building hierarchies. The resulting DT facilitates indoor navigation, space optimization, and asset management, illustrating the practical benefits of integrating structural, functional, and organizational data within a single platform. This framework provides a foundation for informed planning, operational efficiency, and sustainable management practices in small- to medium-sized organizational contexts. However, the study was limited by the exclusion of AI-driven predictive analytics and confidentiality constraints regarding direct energy metrics; therefore, future research should prioritize longitudinal studies correlating real-time positioning with utility smart-metering to empirically quantify energy demand reduction.
Trustworthy efficient learning with graph-aware quantum-classical transformers for hyperspectral imaging and NLP tasks
(2026) Lunia, Jay Vinit; Bin Ahmed, Saad
This thesis focuses on one practical goal: building parameter-efficient models that keep performance close to current baseline models. The prime motivation is to control information flow explicitly, instead of allowing model capacity to grow without accounting for parameter size. In this work, the potential of hyperspectral imagery is exploited by integrating patch-level graph construction with a transformer-inspired attention module to preserve local spatial coherence, so that long-range spectral interactions can still be modeled. The proposed work is further extended to multitask prediction by adapting the architecture after including an attention head in the presented framework. The model uses approximately 3.3 million parameters, which increases the overall model size. In deep learning tasks, models with fewer parameters are preferred when they can provide a similar level of efficiency to larger models. Hence, a quantum-inspired classical deep learning architecture is presented. Using this architecture, low-dimensional quantum encoding is considered, which reduces the number of parameters to 35,000 while maintaining a similar level of performance to the model without the quantum component. To evaluate the strength of the presented model on Natural Language Processing (NLP) tasks, the same quantum model is used with a frozen pretrained encoder, i.e., DistilBERT, and then matched low-dimensional heads, so that compact classical and quantum heads can be compared for non-image tasks such as NLP. The work follows a clear three-stage path. First, PatchGraph-MTFormer is developed for hyperspectral image classification, where locality is important and training data are often limited. It is evaluated on four standard HSI datasets: Indian Pines, Pavia University, Houston 2013, and WHU-Hi-LongKou. Second, QuantFormer is introduced to test whether a compact quantum– classical bottleneck can reduce model size while keeping useful predictive quality in HSI settings. Third, the same bottleneck idea is transferred to NLP using a frozen encoder and matched low-dimensional heads, so compact classical and compact quantum heads can be compared fairly. Across these stages, experiments show a consistent pattern: careful constraints on representations can reduce parameter burden while preserving strong results. PatchGraph-MTFormer reaches OA values of 99.93% (Indian Pines), 99.74% (Pavia University), 100.00% (Houston 2013), and 99.65% (WHU-Hi-LongKou), with 91.50% OA on HyperLeaf cultivar classification, while QuantFormer remains strong with above 99% OA on three airborne HSI benchmarks and about 89.8% OA on EuroSAT_MS. Overall, the thesis provides a practical design-and-evaluation workflow for constrained learning: define bottlenecks clearly, keep protocols fixed, compare under matched settings, and report predictive quality together with parameter and runtime cost.