eScholar
eScholar stores, preserves and disseminates digital copies of the research and scholarly output of eScholar faculty, researchers and students. These can include the following items:
- Monographs
- Pre- and post-prints of academic journal articles
- Theses and dissertations
- Major projects and papers
- Reports/working papers and conference proceedings
Materials in eScholar are openly available to the world and discoverable through search engines such as Google Scholar. This high visibility, discoverability, and exposure can lead to increased citation.
Communities in eScholar
Select a community to browse its collections.
- Ontario Tech Campus Libraries
- Faculty of Business & Information Technology (FBIT)
- Faculty of Education (FEDU)
- Faculty of Energy Systems & Nuclear Science (FESNS)
- Faculty of Engineering & Applied Science (FEAS)
Recent Submissions
Critical digital literacies used by adolescents and young adults with mental health diagnoses while consuming TikTok: a scoping review
(2024-07-01) Slongo, Lea; Morrison, Laura; Hughes, Janette
Teens and young adults with mental health diagnoses utilize social media for information related to their diagnoses and experiences. The design and operationalization of social media platforms can be both helpful and detrimental to this population. TikTok requires that adolescents and young adults have and utilize digital literacy skills to navigate content. This scoping review is interested in exploring how teens and young adults utilize critical digital literacy skills when navigating mental health information on TikTok. As this is an emerging technology and area of study, a large gap in the literature was identified as an opportunity for future research.
"Sounding out solutions: current status of ocean noise pollution and management approaches to conserve cetacean with the focus on Canadian habitats."
(2024-03-01) Nassim, Nakissa; Jawad, Dima
As human activities continue to rise, the adverse consequences of ocean noise pollution on cetaceans (Cetacean is the general noun used to describe all 90 species of whales, dolphins, and porpoises), including stranding, masking, alterations in foraging behaviors, and mating disruptions, have become a significant concern for marine conservationists. Despite international recognition, the complex nature of sound propagation in aquatic environments and diverse contributing sources have left various aspects of ocean noise pollution needing to be fully comprehended. This research project delves into the multifaceted impacts of underwater noise on cetaceans, underscoring the imperative to implement effective mitigation measures promptly. Key strategies, such as planning, resource management, raising stakeholders’ awareness and engagement, adopting noise reduction technologies, and integrating them with existing ones, establishing Marine Protected Areas (MPAs), and initiatives to decrease ship speed and reroute ship lanes, play a pivotal role in alleviating ocean noise pollution.
The project emphasizes the importance of a holistic approach to address the issue, suggesting integrating noise reduction strategies into all conservation plans for seas and oceans. Recognizing the significance of engagement from both the public and private sectors, a fundamental aspect of this approach involves raising awareness to foster a shared sense of responsibility toward marine life. Furthermore, it advocates for intensified research endeavors to further our understanding of the specific impacts of noise pollution on distinct cetacean groups, enabling the refinement of mitigation strategies accordingly.
This project underscores the urgent and collective need for immediate action to tackle ocean noise pollution. By adaptive management, amalgamating technological advancements, regulatory measures, and public awareness and engagement initiatives, we can promote more sustainable coexistence between human activities and the marine environment, ensuring the safeguarding and conservation of cetacean species in our oceans.
Enhancing nursing education: the role of emotional engagement in e-learning
(2024-06-01) Mnaymneh, Marvin; van Oostveen, Roland
This study at Ontario Tech University explores the impact of e-learning on nursing students' professional learning, focusing on digital literacy, emotional engagement, and satisfaction. Utilizing a mixed-methods approach, the research involved a Basic Demographic Survey (BDS), a Digital Competency Profiler (DCP) survey, and e-learning training sessions. Key findings indicate a positive correlation between satisfaction and perceived efficacy, with valuable and user-friendly e-learning platforms enhancing satisfaction. Emotional engagement, influenced by multimedia elements like videos and simulations, significantly impacted satisfaction. The study underscores the importance of incorporating emotional intelligence into e-learning design, recommending user-friendly, interactive e-learning environments to improve learning outcomes.
Multiscale video transformers for video class agnostic segmentation in an autonomous driving setting
(2024-08-24) Cheshmi, Leila; Siam, Mennatullah
Semantic segmentation is a key technique in the perception of autonomous driving. Traditional semantic segmentation models, however, are constrained by the need for extensive annotated datasets and struggle with unknown classes not encountered during training. On the other hand, video class-agnostic segmentation aims to segment objects without relying on their semantic category. Motion cues could be used towards that goal to account for objects outside the closed set of training classes. This project proposes an innovative approach to video class-agnostic segmentation in autonomous driving using multiscale video transformers. We enhance the Video Class Agnostic Segmentation (VCAS) dataset by integrating richer annotations and tracking data from the TAO-VOS (BDD) dataset, thereby providing a comprehensive dataset for better generalization in complex driving scenarios.
Our project involves designing a novel multi-scale video transformer-based architecture that foregoes optical flow, focusing instead on learning motion implicitly to identify objects in a class-agnostic manner. This architecture utilizes the Multiscale Encoder-Decoder Video Transformer (MED-VT) framework, which processes sequential data in a multiscale approach to capture both fine and coarse-grained information. Video transformers utilize encoder and decoder components, along with attention mechanisms, to efficiently process sequential data. Our approach takes an input clip and outputs the class-agnostic segmentation of moving objects in that clip. Features extracted from the raw input clip using a convolutional backbone are treated as tokens and provided to the multiscale transformer for pixel-wise classification. Additionally, we augment the currently available video class-agnostic segmentation datasets with TAO-VOS (BDD) datasets. We also label some missing objects in TAO-VOS (BDD) datasets with a standard semantic segmentation annotation tool in a few of the sequences.
The outcomes of this project include a more diverse and comprehensive dataset and a superior video class-agnostic segmentation model with improved accuracy in mean intersection over union (mIoU). Our training on datasets focused on autonomous driving scenes demonstrated a significant improvement in mIoU compared to models trained on general-purpose video object segmentation datasets.
Energy planning with hydrogen deployment strategies within interconnected infrastructures using enhanced SWITCH model
(2024-07-01) Villalobos Herra, Elena; Gaber, Hossam
The water dimension is not adequately considered in energy models when planning for hydrogen technologies. To overcome this, three novel modules have been developed for the SWITCH energy model: one that considers water drinking systems, a second module that optimizes the size/location/type of hydrogen plants, and the buildings module to integrate buildings using hydrogen-based combined heat and power systems. The modules contribute to the research community by linking the water, hydrogen and power sectors in an energy model.
The modules were tested in a case study for Durham Region, using data from 2022. The main results show that the zone of Oshawa is optimal for building a hydrogen electrolysis plant, but facing drastic changes in its power and water demands. Results also show hydrogen-based combined heat and power systems would not be economically feasible unless the price of hydrogen per kilogram is less than CAD$2.13, considering the 2022 parameters.