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
Development and evaluation summaries of a percutaneous nephrolithotomy (PCNL) surgical simulator
(2024-05-01) Sainsbury, Ben; Green, Mark; Ren, Jing
Traditional surgical training methods, such as the use of cadavers, and porcine models (pigs) fall short of providing the realistic, real-time feedback necessary for mastering complex procedures. Virtual reality (VR) simulators with haptics offer a significant improvement, delivering immersive and interactive experiences that closely mimic real-life surgeries. Cadavers are also costly, averaging $1,300 each, and are in limited supply.
Simulation devices for teaching surgical skills have been shown to significantly reduce surgical errors, enhancing both the safety and effectiveness of surgical training (Anderson & Abrahamson, 2017; Badash et al., 2016; Bushey, 2013; Chen et al., 2021; Chiang et al., 2013; Fried et al., 2005; Pottle, 2019; Sommer et al., 2021). While many existing surgical simulators incorporate elements of VR and haptics, they often lack full integration of a 3D VR operating environment that immerses surgeons completely in the procedural context.
The Marion K181 PCNL Simulator addresses this gap by providing a comprehensive VR surgical simulation platform that integrates advanced haptic feedback and accurate 3D models to create a realistic training environment. This simulator aims to reduce the significant number of deaths caused by medical errors in the US, which total approximately 251,000 annually (James, 2013). These errors lead to additional surgeries, lower patient quality of life, and substantial costs to the healthcare system, estimated at $5.95 billion per year (Badash et al., 2016).
The evaluation of the Marion K181 simulator demonstrates its effectiveness in improving surgical skills, with users reporting high levels of satisfaction with the realism of the haptic feedback and the accuracy of the anatomical models. This platform not only enhances the technical abilities of surgeons but also provides a cost-effective, ethical, and sustainable solution for surgical training.
Police perception of danger
(2024-08-01) Ouellet, Michael; Frederick, Tyler
Previous research has highlighted the relationship between danger, stress, and the well-being of police officers. Still, gaps remain in understanding officers' perceptions of danger. This study addresses these gaps by exploring the concept of subjective danger perception, a cognitive and emotional response to potential physical or psychological harm. The study identifies that environmental cues influence danger perception and exist on a spectrum shaped by social and personal factors. Firstly, this research modernizes the understanding of subejctive danger perception in policing, which was previously limited and outdated. Secondly, it expands the focus beyond physical injury to include the emotional aspects of danger, identifying stress, anxiety, worry, and fear as key components. Thirdly, it adopts a multi-level approach, exploring situational factors and structural, cultural, and institutional influences on danger perception. Additionally, this study addresses the need for a comprehensive theory in policing by advocating for the social-ecological model. Thus, this research provides a richer, more nuanced understanding of how police officers perceive danger and the impact of police culture. Considering the improvement of the safety and well-being of police officers and their communities, this research used a mixed-method approach to explore police officers in Quebec and Ontario. ANOVA and Spearman’s Rho correlation were used to determine group differences in age/length of service, micro-systems, and situational factors. Furthermore, a thematic analysis will provide rich insights into the different aspects that impact police officers' perception of danger. The study's results determined that the key factors that impacted police officers' perception of danger relate to the individual factors of age and length of service (experience and knowledge). Additionally, more proximal factors (micro-systems) to the individual in the social-ecological model had a bigger impact on police officer's perception of danger than the more distal (exo and macro systems) factors. In brief, this research revealed that further research needs to be conducted on the perception of danger, which could help develop better policies for using force and increase officers' physical and mental well-being.
Unified data management in collaborative IoT systems
(2024-08-01) Ouda, Hossameldin; Elgazzar, Khalid
Data has become a central focus for industries and businesses in delivering services. Furthermore, efficient collaboration solutions for IoT systems are increasingly needed to optimize IoT data usage across various domains. This work provides a comparative analysis between data models for handling heterogeneous IoT data. The analysis shows that the document data model outperforms its competitors with a throughput rate of 597.2/sec and an average end-to-end execution time of 0.54 seconds in CRUD operations on IoT data. Furthermore, the work introduces a data unification framework that standardizes IoT device messages sent to the MQTT broker, eliminating the need to read extensive documentation. The framework includes a data interpreter approach that unifies heterogeneous IoT data into a well-defined JSON document the sensing information along with the metadata for the corresponding IoT device, storing it in data modelling collections. The results show an optimization of 88% in the IoT devices profile more than state-of-the-art solutions like Iotivity and SensorML. Also, Interoperability tests have been carried out in the area of smart intersections and their collaboration with weather and healthcare systems. The tests confirm the framework’s versatility and reliability thus proving its suitability for adoption by the developing community.
Development and experimental investigation of direct urea fuel cells
(2024-06-01) Meke, Ayse Sinem; Dincer, Ibrahim
This thesis study considers the development and experimental investigation of Direct Urea-Hydrogen Peroxide Fuel Cells (DUHPFC). The research focuses on preparing electrodes using nickel zinc iron oxide on stainless steel foil and evaluates single cells and stacks under varying conditions. Optimal single-cell performance is achieved at 65°C with 9 M KOH and 0.5 M urea, yielding a peak power density of 46.38 mW/cm². The stack shows improved performance at 65°C, with a power output of 0.307 kW. The single cell attains an open circuit voltage (OCV) of 0.72 V, while the stack reaches 8.8 V. The energy and exergy efficiencies are 58% and 24% at 5 M KOH for single cells, and 48% and 41% at 65°C for stack, respectively. The electrochemical impedance spectroscopy (EIS) shows impedance reduction from 30 ohm.cm² at 25°C to 15 ohm.cm² at 65°C, indicating enhanced ionic conductivity and reduced resistance. These findings provide insights for advancing DUHPFC technology.
Context-aware pedestrian intent prediction for connected and automated vehicles
(2024-08-01) Abdelkader, Ghadeer; Elgazzar, Khalid; Khamis, Alaa
The safety of pedestrians crossing intersections continues to raise concerns since a continuous level of awareness should be maintained to achieve optimal safety. Advanced safety features in assisted and self-driving vehicles have the potential to augment situational awareness and improve the safety of road users. Detecting pedestrian intention is a key element in providing safer urban environments to automated vehicles. However, the complexity involved in anticipating pedestrian crossing intentions makes the task challenging, as these are internal states characterized by dynamic, non-verbal signals, and unpredictable or sudden movements. Such intricacies can lead to misunderstandings for autonomous vehicles, potentially leading to a higher incidence of pedestrian accidents. To achieve this target, there is a need for real-time crossing intentions for pedestrians.
In recent years, there has been an increasing shift towards a new trend in extending deep neural networks into non-Euclidean spaces, commonly known as geometric deep learning. Research in deep learning on graphs is gaining momentum, showcasing the powerful descriptive capabilities of graph structures. These structures provide essential relationship data among various elements, proving invaluable across diverse learning applications. Our study introduces an innovative spatio-temporal graph-based model designed to extract and leverage spatial and temporal features from spatio-temporal graph data. The goal is to uncover hidden patterns within these graphs, which is crucial for accurately predicting pedestrian crossing intentions.
By integrating both visual and contextual information, we present a unique approach to structuring traffic scenes as graph data. This involves a novel ego-centric vehicle approach with a wheel topology that effectively captures the dynamics and interactions among traffic participants, the surrounding environment, and pedestrians.
Additionally, we explore the most impactful nodes that significantly influence the model’s predictive accuracy. The model was tested on three datasets, demonstrating promising results in predicting pedestrian crossing intentions across various urban traffic scenarios.