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Improving speed of transmission line protection: exploring the use of dynamic phasors, transients, and incremental components
No full textHigh penetration of inverter-based resources (IBRs) in modern power grids reduces system inertia and short circuit levels, emphasizing the need for fast and reliable transmission line protection to maintain grid stability. This thesis presents novel algorithms to improve the speed and reliability of line distance protection and faulted phase selection.
This thesis explores two new approaches to improve the speed of line distance protection. The first algorithm uses the dynamic phasor model of a transmission line and the dynamic phasors of the measured currents and voltages to estimate the impedance from the relay location to the fault point. The second line protection algorithm uses the Clarke equivalent networks of a transmission line and the Clarke components of instantaneous incremental currents to estimate the inductance and the resistance from the relay location to the fault point and compare them against the reach settings. The algorithm employs least squares estimation to determine fault loop inductance and resistance. This algorithm is faster than the traditional phasor-based protection algorithms and remains consistent irrespective of fault location and fault resistance while maintaining equal or better dependability and security.
The thesis also examines the problem of determining fault type and faulted phases and proposes two new algorithms. The first algorithm uses current transients in a selected frequency band. A set of indices is computed considering various ratios of the rate of change of the Clarke components of these bandpass-filtered currents. The algorithm makes the decisions based on the values of these indices calculated within a short time window of 0.75 ms after detecting a fault. The second algorithm determines the faulted phases based on the ratios of the rate of change of instantaneous incremental currents computed using a pair of phases at a time, offering simpler implementation and reliable identification within 6 ms.
The performance of the proposed algorithms was validated through extensive simulation studies. The new protection algorithms presented in this thesis can improve the speed of operation of transmission line protection without compromising reliability and requiring expensive highly specialized hardware.Mitacs
Kalkitech Pvt. Ltd
ERL Phase Power TechnologiesFebruary 202
An improved Cox proportional hazards model for reliability analysis of aviation gas turbines considering varying environmental conditions and operational settings
No full textAviation gas turbines are critical components in the aerospace industry, where reliability directly influences maintenance costs, operational efficiency, and safety. The Cox proportional hazards model (Cox model) is crucial in reliability analysis, traditionally employed to evaluate the impact of multiple covariates on the likelihood of failure. However, its application in aviation gas turbines has revealed limitations, particularly when working conditions and gas path parameters are used as direct covariates. These factors, while relevant, often fail to fully capture the degradation process due to their sensitivity to varying working conditions.
In recent years, the integration of machine learning techniques with the Cox model has gained traction, offering a potential pathway to enhance the accuracy of reliability predictions. Despite this progress, existing approaches still predominantly rely on direct covariates that may not accurately reflect the underlying degradation of gas turbines. This thesis proposes a novel method that leverages machine learning to generate new, more robust covariates for the Cox model. By first modeling a healthy gas turbine, the method produces covariates that are less influenced by the variability in working conditions, leading to a more precise representation of turbine degradation over time.
The thesis explores the theoretical foundations of this approach, detailing how machine learning algorithms can be employed to model a healthy gas turbine and subsequently generate new covariates that better align with the actual degradation process. This method addresses the inherent limitations of conventional covariates, providing a more comprehensive method for reliability analysis under varying operational scenarios.
The effectiveness of the proposed method is validated through extensive experiments using the NASA C-MAPSS dataset, which includes data from turbofan engines operating under diverse conditions. Comparative analysis demonstrates that the improved Cox model, enhanced with machine learning-derived covariates, offers better accuracy in predicting gas turbine reliability compared to the traditional model. The results demonstrate the potential of this approach to significantly improve reliability assessments in the aerospace industry, ultimately contributing to safer and more efficient operations.
This research not only advances the application of the Cox model in the context of aviation gas turbines but also opens new avenues for integrating machine learning to generate new covariates with traditional reliability analysis techniques. The findings have broader implications for the aerospace industry and could inform future developments in turbine maintenance strategies and operational practices.May 202
An exploratory study of visitation shelters for long-term care homes
No full textThe province of Manitoba provided 88 long-term care (LTC) homes with 105 external visitation shelters to facilitate resident visits during the pandemic. Shipping containers were fitted with finishes, furnishing and ventilation systems, and connected to LTC homes. This research was conducted using document analysis, government communications, surveys, and field measurements to learn about the visitation shelters related to their: design, implementation, performance, and user experiences. Media and government information provided many details on design/implementation considerations, utilization rates, and challenges. User surveys revealed that shelters made a difference but were institutional/sterile and not conducive to supporting meaningful connections. All data indicated that the shelters were under-utilized. Field measurements indicated substantial air exchange, but high ventilation-associated noise. The lighting colour was appropriate, but lighting levels were relatively low. This study describes the advantages and disadvantages of using external visitation shelters during a pandemic to facilitate LTC visits while attempting to prevent infection
Exploring the effects of enzymatic and thermal treatments on banana starch characteristics
No full textBanana starch has a highly resistant starch (RS) and slow-digested starch (SDS) content, making it attractive as a functional ingredient. Unfortunately, banana starch requires modification processes due to the loss of RS and SDS during gelatinization because of its thermolabile characteristics. This study explores the effect of banana starch modification by enzymatic, heat moisture treatment (HMT) and dual modification (HMT+ enzymatic) on its nutritional (RS, SDS) and functional properties hydration, structural, gelation, rheological). HMT and dual modifications decrease RS (from 44.62 g/100 g to 16.62 and 26.66 g/100 g, respectively) and increase SDS (from 21.72 g/100 g to 33.91 and 26.95 g/100 g, respectively) in raw starch but induce structural changes that enhance RS (from 3.10 g/100 g to 3.94 and 4.4 g/100 g, respectively) and SDS (from 2.58 g/100 g to 9.58 and 11.48 g/100 g) thermo-resistance in gelled starch. Also, changes in the functional properties of starches were evidenced, such as weaker gels (hardness1.77 g/100 g) and increased gelatinization temperature. Improved gelatinization temperature and RS thermostability resulted from modifications that could expand banana starch applications as a beverage and compote thickener
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Perceived artificial intelligence readiness in medical and health sciences education: a survey study of students in Saudi Arabia
Get PDFAbstract Background As artificial intelligence (AI) becomes increasingly integral to healthcare, preparing medical and health sciences students to engage with AI technologies is critical. Objectives This study investigates the perceived AI readiness of medical and health sciences students in Saudi Arabia, focusing on four domains: cognition, ability, vision, and ethical perspectives, using the Medical Artificial Intelligences Readiness Scale for Medical Students (MAIRS-MS). Methods A cross-sectional survey was conducted between October and November 2023, targeting students from various universities and medical schools in Saudi Arabia. A total of 1,221 students e-consented to participate. Data were collected via a 20-minute Google Form survey, incorporating a 22-item MAIRS-MS scale. Descriptive and multivariate statistical analyses were performed using Stata version 16.0. Cronbach alpha was calculated to ensure reliability, and least squares linear regression was used to explore relationships between students’ demographics and their AI readiness scores. Results The overall mean AI readiness score was 62 out of 110, indicating a moderate level of readiness. Domain-specific scores revealed generally consistent levels of readiness: cognition (58%, 23.2/40), ability (57%, 22.8/40), vision (54%, 8.1/15) and ethics (57%, 8.5/15). Nearly 44.5% of students believed AI-related courses should be mandatory whereas only 41% reported having such a required course in their program. Conclusions Medical and health sciences students in Saudi Arabia demonstrate moderate AI readiness across cognition, ability, vision, and ethics, indicating both a solid foundation and areas for growth. Enhancing AI curricula and emphasizing practical, ethical, and forward-thinking skills can better equip future healthcare professionals for an AI-driven future
The employment preferences of young people in Canada: a discrete choice experiment
Get PDFAbstract Background Young people across the world are facing numerous challenges, with unemployment and precarious employment being substantial issues, impacting young people with all levels of education. For many young people, the pandemic exacerbated their employment precarity. While efforts were made to ameliorate these pandemic related challenges for young people, information about the employment preferences of Canadian young workers (YW) is limited. The aim of this study was to understand the employment needs, challenges and preferences of Canadian YW in the COVID-19 era and beyond. Methods Using discrete choice experiment, YW from across Canada aged 18–29 years old were recruited to participate in an online survey October 2022 to April 2023 which was offered in both English and French. Nine job attributes were identified based on findings from the qualitative component of this mixed methods project: wage, earnings stability, job flexibility, vacation, sick time, health insurance, and workplace policies (respectful workplace, and being valued and understood as an employee). Respondents were presented with nine choice sets, each representing two scenarios that differ on policies or actions (attributes) related to their employment during the COVID-19 pandemic. Results Based on the respondent (N = 231) sample, analysis revealed that of YW aged 18–29 years, most valued having employment benefits along with workplace policies. These values were strongest for women and 18–21-year-olds. Overall, the employment preferences of Canadian YW in the current study align with four of five attributes considered by the International Labour Organization as minimum standards for decent work. These include adequate compensation, adequate access to health care, adequate free time and rest, and organizational values that support one’s [own and] family values. More specifically, study findings show that within the cohort there are strong gendered and aged-based preferences for non-monetary over monetary job attributes. These include employment benefits along with equitable, supportive employment policies. Conclusions The findings suggest that health and wellbeing are highly valued by YW and are among key drivers of employment preferences for Canadian YW during and after the pandemic, and therefore call for policies in the workplace that support the health and well-being of YW
An interpretable deep geometric learning model to predict the effects of mutations on protein–protein interactions using large-scale protein language model
Get PDFAbstract Protein–protein interactions (PPIs) are central to the mechanisms of signaling pathways and immune responses, which can help us understand disease etiology. Therefore, there is a significant need for efficient and rapid automated approaches to predict changes in PPIs. In recent years, there has been a significant increase in applying deep learning techniques to predict changes in binding affinity between the original protein complex and its mutant variants. Particularly, the adoption of graph neural networks (GNNs) has gained prominence for their ability to learn representations of protein–protein complexes. However, the conventional GNNs have mainly concentrated on capturing local features, often disregarding the interactions among distant elements that hold potential important information. In this study, we have developed a transformer-based graph neural network to extract features of the mutant segment from the three-dimensional structure of protein–protein complexes. By embracing both local and global features, the approach ensures a more comprehensive understanding of the intricate relationships, thus promising more accurate predictions of binding affinity changes. To enhance the representation capability of protein features, we incorporate a large-scale pre-trained protein language model into our approach and employ the global protein feature it provides. The proposed model is shown to be able to predict the mutation changes in binding affinity with a root mean square error of 1.10 and a Pearson correlation coefficient of near 0.71, as demonstrated by performance on test and validation cases. Our experiments on all five datasets, including both single mutant and multiple mutant cases, demonstrate that our model outperforms four state-of-the-art baseline methods, and the efficacy was subjected to comprehensive experimental evaluation. Our study introduces a transformer-based graph neural network approach to accurately predict changes in protein–protein interactions (PPIs). By integrating local and global features and leveraging pretrained protein language models, our model outperforms state-of-the-art methods across diverse datasets. The results of this study can provide new views for studying immune responses and disease etiology related to protein mutations. Furthermore, this approach may contribute to other biological or biochemical studies related to PPIs. Scientific contribution Our scientific contribution lies in the development of a novel transformer-based graph neural network tailored to predict changes in protein–protein interactions (PPIs) with excellent accuracy. By seamlessly integrating both local and global features extracted from the three-dimensional structure of protein–protein complexes, and leveraging the rich representations provided by pretrained protein language models, our approach surpasses existing methods across diverse datasets. Our findings may offer novel insights for the understanding of complex disease etiology associated with protein mutations. The novel tool can be applicable to various biological and biochemical investigations involving protein mutations
Mechanisms of antigen-dependent resistance to chimeric antigen receptor (CAR)-T cell therapies
Get PDFAbstract Cancer immunotherapy has reshaped the landscape of cancer treatment over the past decades. Genetic manipulation of T cells to express synthetic receptors, known as chimeric antigen receptors (CAR), has led to the creation of tremendous commercial and therapeutic success for the treatment of certain hematologic malignancies. However, since the engagement of CAR-T cells with their respective antigens is solely what triggers their cytotoxic reactions against target cells, the slightest changes to the availability and/or structure of the target antigen often result in the incapacitation of CAR-T cells to enforce tumoricidal responses. This results in the resistance of tumor cells to a particular CAR-T cell therapy that requires meticulous heeding to sustain remissions in cancer patients. In this review, we highlight the antigen-dependent resistance mechanisms by which tumor cells dodge being recognized and targeted by CAR-T cells. Moreover, since substituting the target antigen is the most potent strategy for overcoming antigen-dependent disease relapse, we tend to highlight the current status of some target antigens that might be considered suitable alternatives to the currently available antigens in various cancers. We also propose target antigens whose targeting might reduce the off-tumor adverse events of CAR-T cells in certain malignancies
Creating accessible and inclusive undergraduate studentship opportunities: the ENRRICH experience
Get PDFAbstract Recognizing the systemic exclusion of structurally oppressed groups from academic awards, the ENRRICH (Excellence in Neurodevelopment and Rehabilitation Research In Child Health) summer studentship emphasized the inclusion of structurally oppressed groups. Herein, we outline the processes in creating this funding opportunity, and plans for improvement, including enhanced representation among supervisors.Children’s Hospital Foundation of Manitoba
Children’s Rehabilitation Foundation (Manitoba)
Blennerhassett Family Foundatio
Evaluation of the Technological Performance of Soft Wheat Flours for Fresh-Pasta Production as Affected by Industrial Refining Degree
Get PDFNowadays, whole grain and less refined flours deriving from higher extraction rate milling processes have received much attention due to the presence of the external parts of the grain constituting the bran, with well-known health benefits. The use of these flours can represent a rational option for the valorization of native bran with minimal by-product generation while improving the nutritional and functional profile of the end products. This work aims to evaluate the techno-functional characteristics of commercial soft wheat flours with different refining degrees (proximate composition, functional, rheological, and starch-related properties) and their relation to the produced fresh-pasta quality (cooking behavior, mechanical and optical properties, and sensory assessment). Specifically, water holding capacity, fat absorption capacity, and swelling ability of flours gradually decreased with the refining degree (up to 25%, 16%, and 36%, respectively). Regarding the starch properties, the overall gelatinization process resulted to be negatively influenced by higher extraction rates, leading to a lower consistency of the whole grain starch gels (~17% in the maximum force during heating and ~12.39% peak viscosity). Cooked pasta was darker and redder when increasing the extraction rate. In addition, whole grain-based pasta had 42% higher cooking loss, and it was 86% harder and 101% firmer, leading to the production of a less elastic fresh-pasta with lower swelling ability. However, a good quality end product with naturally high nutritional value can be produced with flours with low refining degree. Results are useful to assess the best productive destination of flours basing on their technological properties