Arts-based research and social justice in sport and leisure

The importance of social justice research is well recognised in current times, in particular the need for innovative studies that intervene into the complex challenges faced within twenty-first century societies. Research that makes a difference. Research that works with, on and around the political, economic and sociocultural obstacles that can conspire to inhibit change where it is most badly needed. As the chapters in this Handbook demonstrate, the importance of social justice scholarship across the field of sport, leisure and physical activity is recognised. Here, too, researchers, scholars and activists work in ways that strive to make a positive difference. In this chapter, we consider the utility of arts-based approaches to social justice research and reflect on examples of two film-based projects in sport, leisure and physical activity. We engage with public responses to these examples, demonstrating the personal, social and cultural meaning and impact of the work and showing how arts-based research can generate community, solidarity, and personal or social change. We propose that arts-based research offers a means to radically democratize social justice research and scholarship

Activation of LXRs alleviates neuropathic pain-induced cognitive dysfunction by modulation 2 of microglia polarization and synaptic plasticity via PI3K/AKT pathway.

Background Cognitive dysfunction is one of the most common comorbidities in patients with chronic pain. It has been shown that activation of Liver X receptors (LXRs) plays a potential role in improving cognitive disorders in multiple central nervous diseases by modulating neuroinflammation and synaptic plasticity. In this study, we mainly investigated whether LXRs could reverse cognitive deficits induced by neuropathic pain. Methods The spared nerve injury (SNI) model was established to explore the roles of LXRs in neuropathic pain induced-cognitive dysfunction. Pharmacological activation of LXRs by T0901317 or inhibition by GSK2033 was applied. In addition, the phosphatidylinositol 3-kinase (PI3K) inhibitor LY294002 was administered to examine the downstream mechanism of LXRs. Changes in neuroinflammation, microglia polarization, and synaptic plasticity were assessed using biochemical technologies. Results We found that SNI induced mechanical allodynia and novel object recognition dysfunction in mice, accompanied by the reduction in expression levels of LXRβ, synaptic proteins, and the PI3K/AKT pathway in the hippocampus. Microglia were activated in the hippocampus after SNI, with an increase in M1 phenotype and decrease in M2 phenotype, as well as upregulation of pro-inflammatory cytokines. Activation of LXRs with T0901317 significantly ameliorated SNI-induced cognitive dysfunction including anxiety, learning and memory. Neuroinflammation and microglia M1-polarization also induced by SNI were reversed after using T0901317. Moreover, T0901317 upregulated expression levels of synaptic proteins and phosphorylation of PI3K and AKT. However, administration of the LXRs inhibitor GSK2033 or PI3K inhibitor LY294002 abolished all the protective effects of T0901317 on cognitive dysfunction in SNI mice. Conclusion Our data indicate that LXRs activation alleviated neuropathic pain-induced cognitive dysfunction by modulating microglia polarization, neuroinflammation, and synaptic plasticity via the PI3K/AKT signaling pathway, and thus, LXRs may be identified as potential new targets for pain-related cognitive deficits. Keywords Liver X receptors; Neuropathic pain; Microglia polarization; Cognitive dysfunction; Neuroinflammatio

Patients’ satisfaction with healthcare services among older people with multimorbidity: Subnational gender perspective

The universal use of patient satisfaction as a measure of quality of healthcare cannot be overemphasized, but studies of healthcare satisfaction between older women and older men with multimorbidity in our contemporary society has been questioned over the years. This paper explores the disparities in patient satisfaction between older women and men with multimorbidity in Nigeria using survey data. Methods We analyzed data collected between October 2021 to February 2022 from a cross-section of randomly sampled 734 participants with multimorbidity with age 60 years and above who presented for routine check-ups and consented to participate in the study. The data were entered into JISC online data collection tool and exported to IBM Statistical Package for Social Science (SPSS) version 27 for analysis. Mann-Whitney U test analysis was performed to compare the participant’s mean satisfaction level and gender. Results Despite higher education among males, females utilize healthcare services more. Our study shows that females are less likely to be satisfied with factors that are linked to access and quality of healthcare, and financial burden of medical care. Whereas males are more likely to be satisfied with factors that relate to patient-physician interaction time and patient waiting time and confidence and trust in medical care. Conclusions Female and male patients may have different expectations regarding healthcare, especially in our society where men are more educated, but females utilize the healthcare services more. The development of appropriate strategies for the implementation of knowledge about patient gender differences will be crucial for the delivery of high-quality gender-sensitive healthcare

Active Learning for Left Ventricle Segmentation in Echocardiography

Background and Objective: Training deep learning models for medical image segmentation requires large annotated datasets, which can be expensive and time-consuming to create. Active learning is a promising approach to reduce this burden by strategically selecting the most informative samples for segmentation. This study investigates the use of active learning for efficient left ventricle segmentation in echocardiography with sparse expert annotations. Methods: We adapt and evaluate various sampling techniques, demonstrating their effectiveness in judiciously selecting samples for segmentation. Additionally, we introduce a novel strategy, Optimised Representativeness Sampling, which combines feature-based outliers with the most representative samples to enhance annotation efficiency. Results: Our findings demonstrate a substantial reduction in annotation costs, achieving a remarkable 99% upper bound performance while utilizing only 20% of the labelled data. This equates to a reduction of 1680 images needing annotation within our dataset. When applied to a publicly available dataset, our approach yielded a remarkable 70% reduction in required annotation efforts, representing a significant advancement compared to baseline active learning strategies, which achieved only a 50% reduction. Our experiments highlight the nuanced performance of diverse sampling strategies across datasets within the same domain. Conclusions: The study provides a cost-effective approach to tackle the challenges of limited expert annotations in echocardiography. By introducing a distinct dataset, made publicly available for research purposes, our work contributes to the field’s understanding of efficient annotation strategies in medical image segmentation

Book review: “A Research Agenda for Urban Tourism” edited by Jan van der Borg, Edward Elgar Publishing, 2022

“A Research Agenda for Urban Tourism” is an edited collection that brings together a number of scholars in urban tourism, including well-known names in the field and younger researchers who look at urban tourism from different perspectives. This book is part of the Elgar Research Agendas series, aimed at exploring certain subjects and outlining the future of research in those fields, in this case urban tourism

Developing a Data-Driven AI Model to Enhance Energy Efficiency in UK Residential Buildings

Residential buildings contribute to 30% of the UK’s total final energy consumption. However, with less than one percent of its housing stock being replaced annually, retrofitting existing homes has significant importance in meeting energy efficiency targets. Consequently, many physics-based and data-driven models and tools have been developed to analyse the effects of retrofit strategies from various points of view. This paper aims to develop a data-driven AI model that predicts buildings’ energy performance based on their features under various retrofit scenarios. In this context, four different machine learning models were developed based on the Energy Performance Certificate (EPC) dataset for residential buildings and Standard Assessment Procedure (SAP) guidelines in the UK. Additionally, an interface was designed that enables users to analyse the effect of different retrofit strategies on a building’s energy performance using the developed AI models. The results of this study revealed the artificial neural network as the most accurate predictive model, with a coefficient of determination (R^2) of 0.82 and a mean percentage error of 11.9 percent. However, some conceptual irregularities were observed across all the models when dealing with different retrofit scenarios. In summary, such tools can be further improved to offer a potential alternative or support to physics-based models, enhancing the efficiency of retrofitting processes in buildings. Keywords: machine learning, energy performance certificate, building energy consumptio

Real-time operation of municipal anaerobic digestion using an ensemble data mining framework

This study presents a novel approach for real-time operation of anaerobic digestion using an ensemble decision-making framework composed of weak learner data mining models. The framework utilises simple but practical features such as waste composition, added water and feeding volume to predict biogas yield and to generate an optimised weekly operation pattern to maximise biogas production and minimise operational costs. The effectiveness of this framework is validated through a real-world case study conducted in the UK. Comparative analysis with benchmark models demonstrates a significant improvement in prediction accuracy, increasing from the range of 50–80% with benchmark models to 91% with the proposed framework. The results also show the efficacy of the weekly operation pattern, which leads to a substantial 78% increase in biogas generation during the testing period. Moreover, the pattern contributes to a reduction of 71% in total days required for feeding and 30% in total days required for pre-feeding

Challenges Encountered by Healthcare Professionals as Frontline Fighters during the COVID-19 Pandemic in Bangladesh: A Qualitative Study

Throughout the pandemic, healthcare professionals (HCPs) around the world encountered numerous challenges. This study was conducted in the middle of the pandemic, from June to November 2021, and explored the multiple issues that HCPs faced in Dhaka, Bangladesh. Thirty doctors and nurses, covering a wide range of workplaces and experiences, were interviewed. A qualitative investigation was performed to assess the influence that diverse organizational, familial, social, and religious factors had on their commitment to fulfil their professional duties. Thematic content analysis was performed on the findings. The results emphasize the physical and mental health problems of HCPs, the vital role of organizations in addressing the wellbeing of HCPs, and the necessity of providing training for them, along with workloads and PPE-related problems. It also explores the roles of families, the influence of society, and the impact of religious beliefs on their commitment during the pandemic

Automated mitral inflow Doppler peak velocity measurement using deep learning

Doppler echocardiography is a widely utilised non-invasive imaging modality for assessing the functionality of heart valves, including the mitral valve. Manual assessments of Doppler traces by clinicians introduce variability, prompting the need for automated solutions. This study introduces an innovative deep learning model for automated detection of peak velocity measurements from mitral inflow Doppler images, independent from Electrocardiogram information. A dataset of Doppler images annotated by multiple expert cardiologists was established, serving as a robust benchmark. The model leverages heatmap regression networks, achieving 96% detection accuracy. The model discrepancy with the expert consensus falls comfortably within the range of inter- and intra-observer variability in measuring Doppler peak velocities. The dataset and models are open-source, fostering further research and clinical application

Architectural Strategies for Flood Mitigation in Urban Environments: A Study of Traditional Elements and Contemporary Resilience

Natural disasters cause extensive losses worldwide annually. Flood events are responsible for economic and life-threatening damages[1]. To mitigate flood risks and resulting damages, particularly in the construction of residential buildings, two approaches exist. First: constructing in areas with lower flood susceptibility, and second: implementing architectural solutions to fortify structures against floods and associated hazards. Due to the presence of water resources, rivers, etc., prompting urban expansion due to reasons like transportation, trade, agricultural use, household consumption, etc., construction near rivers and flood-prone areas becomes inevitable[2]. This underscores the importance of the second approach—architectural fortification. In this study, areas highly susceptible to flooding were identified from flood zoning maps using artificial intelligence to adapt these maps and estimate the most hazardous regions[3]. Subsequently, by examining the specific elements of traditional architecture in each of these areas and exploring the cause and function of each element in facing floods over time, attention is given to the particular and regional (indigenous) architectural features that have responded to floods. Finally, appropriate architectural measures and responses to reduce flood risks, such as constructing at elevation or suitable gradients, is combined with early warning systems to provide a proper route for the future construction projects