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Women trail runners' encounters with vulnerability to male harassment in rural off-road spaces
Get PDFThe #metoo movement and high-profile coverage of murders of women in public spaces have reignited investigation of public harassment and women’s actions as they make decisions where and how to engage in outdoor physical activity. This paper draws from the ideas of Lefebvre (1991) and Massey (1994) to understand women trail runners’ spatial experiences in England. Sixteen women who trail run by themselves participated in go-along interviews in their usual running trails. This method allowed participants to recall moments in specific spaces or address spaces that generate particular feelings, and encouraged the researcher to gain a sensory understanding of the spaces which were important to participants. We analyse the production of the trail through runners’ interactions with people and environment inside and outside the trail, and discuss enjoyment as well as perceptions of vulnerability to male harassment and ‘risky’ moments. Ultimately, despite runners regularly feeling vulnerable when running, they refused to stop. At a time when physical activity and natural environments are being promoted as key contributors to personal wellbeing and public health, this research provides evidence of how the production of spaces and safety negotiations affect women’s running experiences
Conspicuous morality and hidden religiosity of the Confucian education revival in contemporary China
No full textThe contemporary revival of Confucian education offers a chance to rethink moral and religious education diversity in China. Moral dynamics are presented as the conspicuous and dominant force driving the expansion of Confucian education. Confucian activists’ moral anxiety about state education and society and desire for the moral upliftment of their offspring motivate them to embrace the Confucian pedagogy of memorization and act to engage their children in the extensive recitation of the classics. However, Confucianism has always held a religious nature, and religious organizations (especially Buddhism and Yiguandao) have played a hidden role in promoting Confucian education. This chapter concludes with the argument that Confucian education manifests itself as an intertwining of conspicuous morality and hidden religiosity in its contemporary revival
Mental health experiences among undergraduate nursing students in a New Zealand tertiary institution: a time for change
Get PDFNursing students in undergraduate programmes exhibit comparable, sometimes higher, levels of poor mental health and substance use compared to the general population; however, this area remains under-researched in New Zealand. The study involved 172 nursing students enrolled in the Bachelor of Nursing programme at one tertiary institution in Auckland, New Zealand. Employing a mixed-methodology approach, a 29-question survey comprising both open and closed questions was administered to explore the students' experiences with mental health and substance use, as well as their access to support services. Quantitative data were analysed using SPSS version 29 descriptive statistics, while a general inductive approach guided the qualitative analysis. A significant proportion of participants (75%) reported experiencing emotional distress during their studies, with anxiety being the most prevalent (78.5%). A smaller percentage disclosed substance use (8.1%) including excessive alcohol use, cannabis use, nicotine use, vaping cannabis and some refusal to reveal substance use. Surprisingly, less than 1% (n = 0.6) utilised institutional support services. Three qualitative themes were identified including emotional distress and associated effects, emotional and psychological impacts on nursing students' academic journey and tertiary support systems. The findings highlight the urgent need to address the mental health and addiction challenges experienced by nursing students, given their potential adverse effects on academic success and overall well-being. Urgent action is needed to integrate mental health training into the curriculum and provide faculty support. In this study, the underutilisation and inadequacy of institutional support services signal a need for institutional reforms to provide access and personalised mental health support to nursing students. Providing essential skills and support for student success contributes to the overall well-being of the nursing workforce
Spatio-temporal patterns of rainfall variability in Bangladesh
Get PDFBangladesh is experiencing a more rapid warming trend compared to the global average, facing significant climate-related risks. This study gave a comprehensive assessment of rainfall variability across the whole Bangladesh during 1989–2022. Annual rainfall in Bangladesh exhibited significant decreasing trends and high oscillation patterns. The multi-scale SPI and SPEI analysis revealed that Bangladesh experienced severe droughts in 1995, 1999, 2018, 2021 and 2022. The DFA revealed that rainfall evolution exhibited significant long-term positive correlation in almost Bangladesh. These results will support policymakers in Bangladesh to develop suitable strategies in mitigating climate change impacts
Successful co-production can help tackle inequalities in maternal health outcomes
No full textThe experience from this project in an ethnically diverse socially disadvantaged community in England showed the power of co-production in fostering inclusivity, engagement, shared understanding and a fair balance of power while developing solutions to tackle maternal health inequalities. While co-production approaches are helpful in ensuring that women from ethnically diverse and socially disadvantaged backgrounds have a voice in their care to maximise positive health outcomes for themselves and their babies, the project demonstrated that the success of the approach depends on a number of factors both in the underlying ethos and the methodology
Synthetic brain images: bridging the gap in brain mapping with generative adversarial model
Get PDFMagnetic Resonance Imaging (MRI) is a vital modality for gaining precise anatomical information, and it plays a significant role in medical imaging for diagnosis and therapy planning. Image synthesis problems have seen a revolution in recent years due to the introduction of deep learning techniques, specifically Generative Adversarial Networks (GANs). This work investigates the use of Deep Convolutional Generative Adversarial Networks (DCGAN) for producing high-fidelity and realistic MRI image slices. The suggested approach uses a dataset with a variety of brain MRI scans to train a DCGAN architecture. While the discriminator network discerns between created and real slices, the generator network learns to synthesise realistic MRI image slices. The generator refines its capacity to generate slices that closely mimic real MRI data through an adversarial training approach. The outcomes demonstrate that the DCGAN promise for a range of uses in medical imaging research, since they show that it can effectively produce MRI image slices if we train them for a consequent number of epochs. This work adds to the expanding corpus of research on the application of deep learning techniques for medical image synthesis. The slices that are could be produced possess the capability to enhance datasets, provide data augmentation in the training of deep learning models, as well as a number of functions are made available to make MRI data cleaning easier, and a three ready to use and clean dataset on the major anatomical plans
An investigation on machine learning models for enhanced thyroid prediction
No full textThe paper aims to enhance the prediction of thyroid diseases by optimizing the deep learning process and tuning hyperparameters. This project utilizes a dataset focused on one of the most critical issues in health care: thyroid disease diagnosis. Data preprocessing, including Z-scale normalization, was applied to reduce overfitting and ensure the significance of feature contributions. Hyperparameter tuning of machine learning (ML) techniques is primarily utilized for Recurrent Neural Networks (RNNs) to optimize training and improve model classification performance. Comparison variables are utilized in ML methods, specifically with the random forest technique, to enhance model performance and accuracy. This work showcases improvements in the analytical framework and establishes a foundation for more efficient and accurate detection of thyroid diseases
Predictive modelling of Air Quality Index (AQI) across diverse cities and states of India using machine learning: investigating the influence of Punjab's stubble burning on AQI variability
Get PDFAir pollution is a common and serious problem nowadays and it cannot be ignored as it has harmful impacts on human health. To address this issue proactively, people should be aware of their surroundings, which means the environment where they survive. With this motive, this research has predicted the AQI based on different air pollutant concentrations in the atmosphere. The dataset used for this research has been taken from the official website of CPCB. The dataset has the air pollutant concentration from 22 different monitoring stations in different cities of Delhi, Haryana, and Punjab. This data is checked for null values and outliers. But, the most important thing to note is the correct understanding and imputation of such values rather than ignoring or doing wrong imputation. The time series data has been used in this research which is tested for stationarity using The Dickey-Fuller test. Further different ML models like CatBoost, XGBoost, Random Forest, SVM regressor, time series model SARIMAX, and deep learning model LSTM have been used to predict AQI. For the performance evaluation of different models, I used MSE, RMSE, MAE, and R2. It is observed that Random Forest performed better as compared to other models
Linking digital technology, omics and education to facilitate global equity
Get PDFThere are many challenges, not least health inequities, global warming, and a rush for growth and economic development. Personalized, precision, and preventative medicine, bringing the latest omics techniques—genomics, transcriptomics, and metabolomics—for individuals allied to personalized prescription and care should help health equity. Digital technologies and artificial intelligence (AI) can help in an understanding of disease processes and in drug development. A holistic approach to the relationship between technology and the environment and clarity about both the positive benefits and negative harms resulting from using digital tools is necessary. We need to focus on the complete human-environmental interface and not just on climate change and carbon. It will be a measure of collaborative civilization if digital technology, omics techniques, and education can be used to promote global equity. Education linking diversities and performance throughout the world will be crucial
Automated evaluation techniques and AI-enhanced methods
No full textThe chapter explores the transformative potential of artificial intelligence (AI) in reshaping assessment, grading, and feedback processes in higher education. They cover real-time feedback mechanisms, AI-driven practices, and evaluation of AI-based assessments, promoting a more equitable, student-centered learning environment. AI is revolutionizing higher education by providing personalized grading criteria, analyzing student data, and adjusting assessment criteria to accommodate diverse learning styles. This approach promotes student engagement, fairness, and equity, enabling educators to tailor teaching strategies and address learning gaps. The chapter emphasizes faculty training and AI-driven enhanced methods
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