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Bridging the gap: a competency framework for non-clinical simulation technicians
Full text linkHealthcare simulation-based education is essential for developing clinical skills in a risk-free environment, relying heavily on simulation technicians for the operation and maintenance of simulation technologies. However, a shortage of qualified technicians, compounded by rapid technological advancements, limits the expansion of SBE across healthcare disciplines. This study addresses this gap by developing a competency framework for undergraduate students aspiring to become simulation technicians.
Through a review of existing literature and expert consultation, the research identifies the key competencies needed for entry-level technicians. The proposed framework offers a standardized model for training and certification, aligning workforce development with industry standards. This study also contributes to the application of competency-based education in healthcare technology roles, providing a structured approach to curriculum development and improving job readiness for non-clinical graduates. Ultimately, the findings aim to enhance workforce preparedness, support international standardization, and improve the scalability of simulation-based education
Developing a predictive model for factors related to risk of aggression in psychiatric inpatients using physiological and clinical data
Full text linkUnpredictable, aggressive behavior in psychiatric inpatients remains a challenge in mental health, emphasizing the negative impact on both patients and staff. With estimates suggesting a significant percentage of patients exhibiting aggression during psychiatric stays, the study employs big data analysis on an actual clinical data set extracted from patients’ medical records to develop a predictive model for clinical aggression risk factors. Retrospective analysis covers variables such as heart rate, blood pressure, age, incidents, medication, hospitalization, suicide risk, education, type of incident, and gender. Statistical analyses, including t-test, stepwise regression, and logistic regression, reveal a significant correlation between a history of aggression and a lower resting heart rate. The final model identifies predictors such as systolic and diastolic blood pressure, medication refusal, and gender. The study highlights the potential of big data in enhancing medical insights and recommends future exploration of streaming and temporal data for more precise disease prevention
Accessible by design: a systematic literature review examining legibility, readability, and the potential of typography
Full text linkAlthough there are many formats for information and communication, printed or digital text remains a primary communication medium. Reading is necessary for locating, understanding, and using information in our personal and professional lives. The importance of reading makes typography essential to accessibility. This systematic literature review includes 59 articles (2000-2024) reporting empirical studies on typography in Latin alphabet-based languages. It examines the effect of character design on legibility and typesetting on readability, the influence of readability on cognition, and the potential benefits of disfluent typography. Key findings are: 1) increased readability benefits cognition; 2) serifs are not a significant legibility factor; 3) no single type size or typeface optimizes readability for everyone in every situation; and 4) familiarity may be a significant legibility and readability factor. The results may guide practice and policy, identify research gaps, and suggest that accessible typography guidelines should reflect the complexity and nuance in optimizing readability
Using AI-Powered Tools to Enhance Article Retrieval Processes: Locating Quality Resources Quickly
Full text linkThis poster examines how AI-powered tools impact the resource retrieval process with an awareness of challenges in digital literacy, critical thinking, and accessibility. Insight within the poster seeks to enhance understanding of how to use AI-powered processes independently or alongside traditional article retrieval platforms to streamline the resource retrieval process. Specifically, it will focus on the ethical integration of AI to reduce the time required to collect the resources in awkward traditional platforms while increasing the time available for learning outcome achievement. This investigation explores the student experience using traditional research repositories and AI-powered networks, comparing them in terms of content quality, efficiency, and engagement
A mathematical model to simulate physiological adjustments to extreme environments to analyse human responses to adverse environmental conditions
Full text linkHumans are among the most adaptable species on Earth, yet extreme environmental conditions impose significant limits to human performance, wellbeing, and survival. The study of human physiology in extreme environments is challenging due to the ethical and practical constraints of conducting experiments on humans in such conditions. Investigating and understanding human physiology and the effects of extreme environments on humans is crucial for the safety and wellbeing of individuals. Information about the physiological responses to extreme environments can be transferred to study the effects of different interventions, the use of protective equipment, or the development of medical treatments. Computational models provide a tool for studying the physiological responses to extreme environments, and for predicting the effects of different interventions. This research aims to develop a computational biomedicine model with application to investigate the impact of underwater exposure on human physiology. Three contributions in this research comprise the development of the computational model, the model validation, and the development of a research infrastructure to facilitate research studies. The developed computational biomedicine model simulates the dynamic interactions between human physiology and extreme environmental conditions. It includes dynamic relationships of blood pressure, ow, and volume, and the effects of underwater exposure in the form of immersion and ambient pressure effects. The model was validated using a sensitivity and plausibility analysis to evaluate the model's predictive capabilities and accuracy to simulate the physiological responses to underwater exposure. The results demonstrated that the model is capable of predicting physiological dynamics for a general population allowing the model to be individualised. It was further demonstrated that the model predicted the physiological responses to underwater exposure in a reasonable physiological range, confirming the model's applicability to scuba diving scenarios. The work further developed an extreme underwater environment research laboratory to conduct underwater exposure experiments and research studies. An underwater exposure protocol was developed defining a recreational diving exposure profile and a list of biomedical measurements to be collected during the underwater exposure. The computational model and the research infrastructure are valuable contributions that invite further research to study the effects of extreme environments on human physiology
Monitoring separation process in separator tanks by ultrasound technique
Full text linkThe focus of this project is to monitor the level of interfaces inside the separation tank during the refining of crude oil by using ultrasound technology. The primary objective is to develop a robust method to measure liquid separation levels in both horizontal and vertical tanks by analyzing the amplitude of ultrasound pulses. The remarkable innovation of this method is the exclusion of the time-of-flight data in the monitoring process. To estimate the liquid level, the traditional method has been to measure the time of flight of ultrasound pulses. However, this project presents a novel approach that solely relies on the analysis of the amplitude of the ultrasound pulses. By studying the maximum amplitude matrix taken from the measurements, one can accurately distinguish the liquids inside the tank and the material type in it.
This creative method has some great advantages when the flight time is excluded. By eliminating time measurements and focusing on amplitude, the process of monitoring is made simpler. In addition, it minimizes potential errors that might be caused by changes in the liquid properties or outside influences that can influence the time-of-flight readings.
An amplitude-based approach provides a real-time, non-invasive solution to monitor liquid levels in the separation tank during refining. By focusing on the amplitude of the ultrasound pulses, this research contributes to developing a more efficient monitoring system.
This project introduces a novel and inventive method for tracking the level of interfaces in the separation tank through ultrasound technology. By exclusively analyzing the amplitude of the ultrasound pulses, this method eliminates the reliance on time-of-flight data, simplifies the monitoring process, and improves accuracy in liquid level measurements. This method is registered as a patent in Brazil
Resilience in silence: schooling while adultified as a Black girl in Canada
Full text linkThis autoethnography examines how adultification and parentification shaped my educational and emotional development as a Black girl raised in a Caribbean family in Canada. Using narrative inquiry, I explore how racialized bias, gendered expectations, intergenerational dynamics, and cultural norms influenced my identity, mental health, and academic path. Four discourses frame this work—racialized bias and inequities, adultification, family dynamics, and resilience—illustrating how early caregiving roles disrupted childhood. While fostering adaptability, adultification also produced emotional suppression and academic detachment. This narrative highlights the consequences of premature responsibility and calls for trauma-informed, culturally responsive education that honours Black girls’ childhoods
EvoGrip: design and model predictive control of a modular, cost-efficient robot hand for research applications
Full text linkThis thesis presents the design and implementation of EvoGrip, a modular 3D-printed robotic hand developed as an open platform for advanced control experimentation. Leveraging open-source designs, EvoGrip enhances accessibility, adaptability, and dexterity. Its mechanical structure, derived from the Inmoov arm, was refined to incorporate position and force sensing with improved finger mechanics for precise and reliable performance. Two modeling strategies and a dual control scheme using Model Predictive Control (MPC) were proposed and experimentally validated. The system achieved accurate finger trajectories and effective disturbance rejection, demonstrating the potential of MPC for robotic hand control. Although some reactive behavior was observed in multi-finger scenarios, decentralized controllers showed promising results, indicating potential for more advanced control strategies. This work establishes a foundation for future extensions, including multi-input, multi-output MPC and reinforcement learning to enhance coordination and robustness. EvoGrip offers a versatile research platform for developing innovative control techniques in humanoid robotic hands
Knowledge management as the next nuclear safety regulatory evolution
Full text linkCovering the creation, use, sharing, and management of information, knowledge management is well known. This is also true in the nuclear sector where knowledge management has been discussed at the international level. However, the shifting landscape of the modern world of work presents risks to organizations.
The nuclear sector has learned paradigm shifting lessons from large events involving nuclear power plants, i.e., the accidents/events at Three Mile Island, Chornobyl, and Fukushima Daiichi, and the conflict in Ukraine and the occupation of the Zaporizhzhia nuclear power plant presents an opportunity for new lessons; a primary of which, with respect to knowledge management, is from the sudden loss of a significant number of staff and their tacit knowledge.
Therefore, it can be posited that knowledge management, in particular the continuity of tacit knowledge within specific individuals, should be considered as a matter of nuclear safety and its regulatory framework
Early detection, classification and proactive mitigation of cyberattacks in microgrids embedded with renewables and electric vehicles charging infrastructure
Full text linkMicrogrids are localized energy systems that can operate independently or in conjunction with the main utility grid. Microgrids incorporate renewable energy resources such as solar panels, wind turbines, and increasingly support the deployment of Electric Vehicle Charging Stations (EVCSs). This integration promotes cleaner energy generation and enhances operational efficiency and reliability. However, the deployment of such advanced technologies necessitates robust communication networks to facilitate data exchange among microgrid components, which exposes them to cybersecurity vulnerabilities. Cyberattacks targeting microgrid infrastructure can severely disrupt operations and cause significant technical and economic damage, emphasizing the urgent need for early detection and proactive mitigation strategies against those attacks. This thesis proposes a novel approach for the early detection, accurate classification, and proactive mitigation against cyberattacks. The methodology begins by analyzing electrical signals by employing an advanced signal processing technique known as Continuous Wavelet Transform (CWT), which converts input signals into time–frequency representations called scalograms. These scalograms accurately visualize and highlight sharp transition events, making them an effective tool for capturing anomalies indicative of cyberattacks. These scalograms are further enhanced using an image processing tool called a morphological operation and the Bartlett observation window to suppress irrelevant variations and emphasize discriminative features. Subsequently, the enhanced scalograms are fed into a Convolutional Neural Network (CNN), which is a class of deep learning, to learn the relevant features from these scalograms and classify different types of cyberattacks. The proposed approach achieved a high detection and classification accuracy of 99.53% and 98.80%, respectively. Moreover, the computational time is accelerated by leveraging the parallel processing capabilities of an advanced Graphics Processing Units (GPUs), achieving a 95.85% reduction in training time. Furthermore, the Long Short-Term Memory (LSTM) network is employed to provide valuable insights of attack patterns, thus, enhancing the transparency, and reliability of CNN's decisions. Finally, a novel technique is developed to proactively mitigate the impacts of such attacks on the operation of the microgrid system