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UNB Scholar is an institutional repository initiative of UNB Libraries intended to collect, preserve, showcase, and promote the open access scholarly output of the UNB community. Use UNB Scholar to explore specific collections, or search all content in the repository. Material submitted to the repository will also be freely discoverable online through Google and other major search engines.
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Recent Submissions
Pre-Service Teachers of French as a Second Language and the Construction of a Professional Identity
(American Educational Research Association, 2022-04-24) Le Bouthillier, Josée; Culligan, Karla; Kristmanson, Paula
This study explores the development of linguistic competence and confidence of pre-service French as a second language (FSL) teachers enrolled in a teacher education program in New Brunswick, Canada. In addition to their regular program requirements, participants in the study engaged in a series of language support sessions. We collected French oral proficiency assessment results pre- and post-sessions and focus group interview data. The analysis shows that participants' language proficiency increased over the course of the sessions and the program. Interview data reveal professional identity-related themes such as confidence, and the importance of safe spaces for interaction and mediation. Implications for FSL teacher recruitment and retention are given, specifically with regard to teacher candidates' need for certain types of support.
Des pratiques d’autorégulation basées sur les forces des élèves
(Association canadienne des professionnels de l'immersion, 2024) Garrett, Melissa Dockrill
Feasibility of producing non-structural wood products using trembling aspen lumber
(University of New Brunswick, 2025-01) Zhang, Mengyuan; Gong, Meng; Chui, Ying-Hei
Trembling aspen (Populus tremuloides) is abundant in Canada but underutilized in non-structural solid wood products. This study was aimed at evaluating the feasibility of using aspen lumber to produce flooring, moulding, and siding. The shrinkage, surface roughness, and wettability of aspen wood were tested. The surface hardness, screw withdrawal resistance, colour change, and dimensional stability of three products fabricated were examined. The wood yield of each product was analyzed. It was found that aspen showed superior machinability and wettability to silver maple and yellow poplar. Aspen flooring had a Brinell hardness of 13.47 MPa, 60% lower than silver maple. Aspen moulding exhibited a screw withdrawal resistance of 23.42 MPa, 15% higher than eastern white pine. Aspen siding showed comparable colour and dimensional stability to spruce-pine-fir wood. The aspen wood yields were estimated to be 38.25%, 25.4%, and 49.2% for flooring, moulding, and siding, suggesting its potential for non-structural applications.
Case studies on the life cycle assessment of lumber production and of tall wood buildings
(University of New Brunswick, 2025-01) Zahabi, Nadia; Gong, Meng; Gu, Hongmei
Mass timber provides a low-carbon alternative to steel and concrete, reducing global warming potential (GWP) and non-renewable energy use while acting as a carbon sink. Life cycle assessment (LCA) evaluates environmental impacts, supporting sustainable construction practices. This research includes three LCA case studies. The first examined softwood lumber production in New Brunswick, Canada, with emissions of 43 kg CO₂ eq/m³, relying on 58% renewable energy from woody biomass during kiln drying. The second analyzed hardwood lumber, emitting 41 kg CO₂ eq/m³ up to sawing, using 98% non-renewable grid energy. Both softwood and hardwood offset their emissions with stored carbon, achieving negative GWP. The third study compared cradle-to-grave impacts of hybrid mass timber-steel, full mass timber, steel, and concrete designs in the Bakers Place project, USA. Mass timber significantly reduced GWP and non-renewable energy but showed higher acidification and eutrophication impacts due to transportation and landfill decomposition.
Exploiting temporal dynamics to improve the robustness of continuous myoelectric control
(University of New Brunswick, 2025-01) Tallam Puranam Raghu, Shriram; Scheme, Erik J.; MacIsaac, Dawn T.
Myoelectric control based on Surface electromyography pattern recognition (sEMG-PR) offers intuitive and dexterous control of powered prostheses for people with limb differences. However, conventional sEMG-PR systems often struggle with transitions between movements, impacting online usability. In this thesis, we investigated these transition-specific challenges and proposed novel approaches to enhance the performance and user experience of sEMG-PR systems.
We first established a comprehensive framework for evaluating classifier performance during transitions, incorporating transition-specific metrics and continuous dynamic datasets. This framework represents an improvement over conventional evaluation methods, which often focus primarily on steady-state performance and neglect transitions. Our analysis, utilizing this enhanced framework, revealed that classifiers, even with similar steady-state performance, can differ substantially in their ability to handle transitions. This finding underscores the limitations of conventional evaluation methods.
Next, we systematically investigated various error-mitigation strategies, including existing and novel post-processing techniques. While some techniques showed promise, particularly those based on rejection, our findings suggest that relying solely on post-hoc error correction may not be sufficient to address the challenges of transitions effectively.
Finally, we explored incorporating continuous dynamic data, inclusive of transitions, into the training process. Our results demonstrated the advantages of leveraging Long Short-Term Memory (LSTM) networks, which can effectively capture the dynamic nature of transitions. Furthermore, we pioneered the use of self-supervised learning for sEMG-PR, and demonstrated its effectiveness in learning meaningful and robust representations from unlabeled continuous dynamic data, leading to enhanced performance both offline and online.
Our findings underscore the crucial role of temporal information, dynamic training data, and appropriate model selection, particularly temporal models like LSTMs, in achieving robust and reliable sEMG-PR based myoelectric control. The proposed approaches have the potential to significantly enhance the usability and effectiveness of these systems, paving the way for more intuitive and user-friendly prosthetic devices.