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Gendering Colonialism: The St Joan’s Social and Political Alliance and the British Imperial Government in the Discourse Around Forced Marriages of African Women, 1935-1939.
Full text linkThe complexity of the British imperial endeavor is explored in this study; this is exemplified by how some elements and activities that can be considered contradictory to the interests of the empire helped to strengthen it instead. In this case, such contradictory elements include the all-women’s group – St. Joan’s Social and Political Alliance. In the same light, the dynamics of gender relations within the British empire are disclosed, shedding more light on the experiences of both British and indigenous women within the imperial apparatus, as well as the patterns of relationship between the two groups of women. It is shown that the Alliance’s humanitarian agitations for imperial reform of marriage practices in favor of African women were inherently imperialistic. Importantly, this study also constitutes a pioneer case-study of the activities of St. Joan’s Social and Political Alliance in British colonial Africa using the debate about forced marriages of African women from 1935 – 1939
Home Care Nurses’ Perceived Competence and Self-Efficacy Regarding Palliative Care: A Cross-Sectional Study
No full textBackground: The demand for palliative care (PC) continues to grow globally due to the increase in the aging population. Integrating community-based PC enables patients to receive care in their homes, promotes family involvement, and is cost-effective. Despite its benefits, nurses report challenges in delivering competent PC, which impacts the quality of care delivered to patients and their families. Objective: This cross-sectional study explored Ontario home care nurses’ perceived level of competence and self-efficacy in PC delivery. Methods: An online survey was created using two validated scales. The surveys were disseminated through home care and professional nursing organizations. Inclusion criteria were 1) Registered Nurses or Registered Practical Nurses, 2) currently working as a home care nurse in Ontario, 3) having at least six months of nursing experience, and 4) having provided PC in patients’ homes. Results: Findings from the survey suggest a positive association (ρ = .69, p < .001) between perceived competence and self-efficacy. Nurses reported the lowest perceived competence was in ‘spiritual care’ and the lowest self-efficacy was in ‘symptom management.' Organizational and workplace environments showed a significant association with nurses’ perceived competence and self-efficacy, further highlighting the importance of hands-on, real-world application. Conclusion: This study underscores the importance of PC education, targeted training, and supportive workplace environments in enhancing nurses’ confidence in PC delivery. These factors support the enhancement of PC delivery, the retention of community PC nurses, and the sustainability of palliative home care
Technology and Human Dignity: The Contemporary Relevance of George Grant's Views
Full text linkGeorge Grant argues that modern innovations in technology are delineated by what he terms ‘the co-penetration of art and science’, which disposes their rational methods towards the satisfaction of while in a purported ‘spirit of creativity’. Though such a spirit has provided many benefits, a natural worry arises as to what may be justified, morally, within the parameters of such creativity. For Grant, such skepticism is well-founded as the gradual expansion of technology is co-measure with ‘demythologization’, that is, the loss of any sense of objective, transcendent purpose. Noting how this worrying trend invites a dangerous premise of making human life subordinate to such creative drives, Grant asserts that the highly individualistic nature of modern technological thinking ultimately challenges the idea of human dignity itself. However, in his Thinking Like a Mall, Steven Vogel argues for the non-existence of nature by attempting to demonstrate that the entire world is simply the result of Man’s artifice. Labelling such projects as technological, Vogel goes on to say that each technology’s ‘wildness’ prevents it from being absorbed into projects of mastery, negating concerns that technology will attempt to master human nature. Yet in presenting Grant’s historical examination of the idea of technology, particularly as it relates to the ideas of ‘progress’ and Nietzsche’s critique of the same, I will argue that Vogel’s view of technology is ultimately inadequate as it does not satisfactorily what Grant identifies as the novelty of current technological thinking, which relates to the profound lack of a ‘myth’ to contextualize our moral decision making in modern technological thinking. Rather, Vogel’s account is rather static inasmuch as it equivocates technology with artifacts and does not pay adequate attention to how the idea of technology has developed, particularly in recent history. As such, Vogel’s moral program fails to address the issues that Grant raises, and thus reinscribes the most harmful aspects of technological thinking
Intelligent Fault Detection in Solar Panels
No full textSustainable IndustryIn this paper, we study recent researches in intelligent fault detection and control systems of solar panels. Solar panels used in buildings are prone to varies faults. These faults can be both electrical and environmental. Electrical faults are mainly due to mismatch in design or simple electrical failure of a cell while, environmental faults are often caused by partial shading and extreme weather conditions. Electrical faults can be categorized into component failure, sustained system isolation, brief system isolation, inverters shutdown, shading, maximum power point tracking unit failure and spot heating. Mentioned faults can greatly impact the efficiency of solar panels. To overcome this issue , faults have to be detected and then compensated. Varies methods of fault detection are proposed by different studies. Simplest method of fault detection involves monitoring the output of panels to look for abnormalities during different hours of operation. Such methods while cheap, lack the ability to provide the type and source of faults. To detect the type of faults, varies studies offer different methods such as ideal cell model based efficiency comparison using artificial intelligence, sensor based monitoring systems and process history based approach are used. While monitoring systems based on efficiency are the cheapest, sensor based monitoring systems are the most accurate. In this study, we propose a new method to increase the efficiency and accuracy of the system even further by mixing all three methods and designing a sensor based intelligent control system. Our proposed system will monitor the output of panels periodically to track the output power for changes. We implemented a sensor based system with a central PLC controller to monitor possible failures in maximum power point tracking units as well as other physical components. Our proposed method results in faster more distinguished failure detection
Exploring the Role of Anomalies and Sparsity in Graph-Based Recommender Systems
No full textSocial Network Analysis (SNA) is a powerful approach for analyzing relationships among entities within networks of varying complexity, enabling the discovery of structural patterns based on real-world data. Despite its numerous applications, recommender systems have emerged as a particularly effective domain of study. These systems have evolved from basic content-based and collaborative filtering methods to sophisticated graph-based methods, powered by strong relationships among users and products. This dissertation explores the role of sparsity and anomalies using graph-based methods. We begin by reviewing the development of recommender systems, with a focus on the growing interest in Graph Neural Network (GNN)–based approaches in recent years, which have significantly improved both recommendation quality and personalization. We then leverage SNA to identify and predict future trends that are likely to shape the future of this field. Drawing from prior research and aiming to highlight their real-world application and societal impact, we first conducted a study on identifying socially isolated individuals in palliative care networks. Social isolation is a public health issue, particularly in high-risk groups such as older adults and palliative care patients. Using social network analysis, our proposed approach maps patient–care provider relationships to an attributed weighted graph and uses outlier detection approaches to detect the people who are at risk of isolation. The project not only demonstrates the feasibility of applying recommender system concepts to identify isolated members but also provides early warnings for interventions in a timely fashion. Followed by the promising results of this initial application, we generalize our research to combine anomaly detection with session-based recommender systems. Anomalous embeddings can have a profound impact on reducing performance in most recommendation tasks, especially for sparse data. Our approach explores the role of anomaly detection approaches in the session-based graph-based recommendation pipeline to improve accuracy. By means of anomaly embedding zeroing out, our approach addresses the effects of noise and enhances predictive accuracy on three diverse real-world datasets. Moreover, to address the sparsity issue, we suggest a cross-domain recommendation approach that employs semantic alignment in combination with clustering approaches with the aim of knowledge transfer between the dense and sparse domains. Cross-domain recommendation effectively bridged the gaps between various user–item interaction patterns for the system to make appropriate recommendations in the case of sparse explicit feedback. Experimental comparisons illustrate the improvements achieved under different conditions
Identifying Therapeutically Targetable Tumour-Immune Interactions in Small Cell Lung Cancer
No full textSmall cell lung cancer (SCLC) is a highly aggressive metastatic lung cancer, accounting for 15% of all cases with poor survival outcomes revealing the necessity to produce novel therapeutic strategies. Recent studies show that SCLC has significant tumour heterogeneity with varying gene expression, presenting an opportunity to use machine learning-driven algorithms with a promising route to uncover its underlying mechanisms. This study leverages single-cell RNA sequencing (scRNA-seq) datasets, and machine learning (ML) to explore tumour heterogeneity, identify biomarkers predictive of novel therapeutic targets, and generate graphical predictions of tumour-immune interactions in SCLC patients. We will use published datasets to identify the cellular basis of tumour-immune interactions and identify gene expression changes within SCLC cells. Pathway analysis and biological validation will extend the results to molecular signalling pathways. Then we will conduct a literature search for the selected genes that are known to disrupt cellular interactions distinctive of SCLC to be further tested by the lung cancer research team in pre-clinical models. Our preliminary analysis shows promising outcomes producing key biomarkers in SCLC stage and treatment groups across immune and epithelial cell subtypes. Genes including RBP1 and CD74 were identified with strong protective effects and further exploration of these genes can highlight specific-stage molecular drivers to guide the ML models. Incorporating advanced models may yield more accurate predictions and improved biomarker discovery with clinical significance. In summary, this study aims to identify novel biomarker targets and therapeutic strategies that can be validated in pre-clinical models and translated into clinical applications
#ad on Instagram: Investigating the promotion of food and beverage products
No full textViable, Healthy and Safe CommunitiesThe recent rise in popularity of social networking sites has led to an associated increase in user-generated photographic content relating to various aspects of users' personal lives. The sharing of food photography has become a popular means of social interaction between friends and strangers online, and has prompted companies in the food and beverage industry to shift their marketing objectives from the traditional top-down strategies to a more modern peer-to-peer approach. The current study investigated the promotion of food and beverage products on Instagram tagged with #ad. Specifically, the current study evaluated aspects of food and beverage images (N = 100) which garnered the most popularity (i.e., likes) among viewers, information about the author (e.g., credentials), as well as cues to like or comment on each image and the audience reaction to images. In evaluating the popularity of food and beverage images, a likes-to-follower ratio was calculated by dividing the number of likes on each image by the number of followers the author of the image had. Findings of this study indicated that images containing beverages, mainly consisting of protein or weight-loss drinks, were more popular compared to advertised food products (p = 0.026). In addition, the majority of authors were not considered credible sources of nutrition information (n = 94), and many did not list credentials (n = 89), indicating that advertised food and beverage products may not fully align with evidence-based guidelines that one would receive from a Registered Dietitian or other healthcare professional. The majority of comments on images were positive (M = 15.0, SD = 24.6), suggesting a low message resistance to food and beverage products advertised on Instagram. Results of this research have implications for public health initiatives targeted towards marketing food and beverage products on social networking sites
Optimizing review-based recommendations using explicit and implicit aspect interactions
No full textIn today’s world, where vast amounts of data are generated daily, providing users with the most relevant information has become increasingly challenging. Recommender systems have therefore attracted significant attention for their ability to predict users’ preferences across a variety of items. While many such systems have been proposed in recent decades, most overlook the benefits of aspect-level review analysis, often leading to suboptimal recommendations. In this thesis, we enhance the performance of review-based recommender systems by integrating both explicit and implicit user–item interactions derived from profiles built on aspect-level sentiments. These profiles are constructed from sentiments expressed in reviews about domain-specific aspects. To achieve this, we leverage DeBERTa (Decoding-enhanced BERT with disentangled attention) for aspect-based sentiment analysis, capturing user preferences from past reviews and item characteristics from public opinion. Our experiments demonstrate that our model outperforms several existing review-based methods by performing fine-grained analysis of reviews, focusing on the most informative segments of the reviews and their associated sentiments, to build robust user and item profiles. This profile construction reduces the system’s reliance on review text, an independence that is particularly valuable in real-world scenarios where predictions must be made for unseen user–item interactions without available reviews
Session 2: Grad Panel: Intimate Partner Sexual Violence Among University Students: What Does it Look Like and How is it Constructed and Supported?
No full textIntimate Partner Sexual Violence Among University Students: What Does it Look Like and How is it Constructed and Supported
Determining the Anti-Cancer Effects of Long Pepper and Synthite Green Tea Extract Alone and in Combination with Standard Chemotherapy Using Two and Three-Dimensional Cell Culture Models of Human Glioblastoma and Neuroblastoma
No full text['UNSDG 3: Good Health and Well-being (https://sdgs.un.org/goals/goal3)', 'UNSDG 10: Reduced Inequalities (https://sdgs.un.org/goals/goal10)']Viable, Healthy and Safe CommunitiesWithin the population that is diagnosed with cancer, most infants have neuroblastoma and most brain cancer patients have glioblastoma. Since conventional chemotherapies such as, temozolomide (TMZ) and cisplatin, target cancerous and healthy cells, the prolonged use of this treatment often results in harmful side-effects. Alternatively, natural health products (NHPs) have anti-cancer activity and non-toxic properties. Thus, our research seeks to determine whether Long Pepper Extract (LPE) and Synthite Green Tea Extract (STE) have selective anti-cancer activity in the treatment of neuroblastoma and glioblastoma. Our in-vitro results suggest that LPE and STE selectively induce apoptosis in U-87 Mg glioblastoma and SH-SY5Y neuroblastoma cells. When using these extracts in combination with standard chemotherapy, our results demonstrate that STE and LPE enhance the anti-cancer activity of TMZ and cisplatin in-vitro. In-vivo trials were completed to test the effect of STEs anti-cancer properties on a group of immunocompromised mice that were subcutaneously xenografted with glioblastoma cancer. Our findings did not express a significant difference in tumor size and volume between the control and treatment groups (LPE, TMZ, and LPE, TMZ treated groups). However, our findings suggest that STE alone is able to reduce tumor volume. Three-dimensional (3D) cell culture models are also being used to mimic in-vivo conditions. If the in-vivo results are similar to our in-vitro results, it could provide a safe alternative to standard treatments for neuroblastoma and glioblastoma