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Tools for accurate tidal volume calculation during out-of-hospital ventilation
No full textBackground: Out-of-hospital cardiac arrest in England affects approximately 60 000 individuals annually, with only 8% surviving to discharge. Accurate tidal volume calculation, based on predicted body weight, is essential to avoid hyperventilation and its associated risks. Aims: This systematic literature review aims to evaluate tools, which estimate height or weight in adults, to determine their suitability for use within prehospital settings, enabling accurate tidal volume calculation. Methods: A systematic literature review was conducted using MEDLINE and CINAHL databases to identify relevant studies on height and weight estimation tools. The review adhered to PRISMA guidelines and assessed study quality using a modified Newcastle-Ottawa Scale. Findings: In the prehospital care setting, three tools – ulna length, tidal tape, and the Modified PAWPER XL-MAC-2 – demonstrated good accuracy for weight estimation, with the Modified PAWPER XL-MAC-2 tool identified as the most likely to yield accurate results given the specific circumstances of prehospital care. Conclusion: Accurate height and weight estimation tools are essential for calculating tidal volumes in the management of out-of-hospital cardiac arrest. While the Modified PAWPER XL-MAC-2 appears effective, further research is needed to confirm its efficacy and practicality in prehospital settings
Attitude Towards Assisted Dying Among Clergy and Lay People in the Church of England
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Attitude towards assisted dying was assessed among 3,230 people who took part in the Church 2024 survey. Asked to respond to the statement ‘I am in favour of allowing assisted dying’, 51% disagreed, 28% agreed and 21% were uncertain, suggesting a sizable minority were either in favour of changing the law or undecided. Those against changing the law tended to agree that it is wrong for someone to take their own life, that only God can give and take life and that the risks of abusing any process are too great. Opinion varied across various groups, with women more in favour than men, the old more in favour than the young, laity more in favour than clergy and Anglo-Catholics or Broad Church more in favour than Evangelicals. Personal and psychological disposition predicted some variations in attitude towards assisted dying, probably because they predisposed individuals to taking more general liberal or conservative stances. The patterns are similar to those seen in several different moral issues debated in the Church of England in the last three decades, suggesting assisted dying might follow a similar trajectory in years to come
Left Ventricular Morphological and Functional Predictors of VO2peak: A three-year observational study
No full textEnhancing Stock Price Prediction Accuracy Through Deep Learning Techniques: A Case Study on Nepal's Stock Market
No full textIn recent years, the emergence of Machine Learning and Deep Learning has substantially improved the precision of stock price prediction, a critical area of interest for economists and investors. This research focuses on Nepal, a developing country with high economic volatility. We use Long Short-Term Memory (LSTM), a form of Recurrent Neural Network (RNN), and data from “sharesansar” to forecast stock prices. Our LSTM model has been modified to reduce losses while maintaining constant accuracy. This enhancement not only advantages experienced traders but also allows beginner traders to engage in lower-risk trading. We use popular metrics to assess the performance of our model, such as Root Mean Square Error (RMSE), Mean Square Error (MSE), and Mean Absolute Error (MAE). These measures are used to compare the accuracy of the LSTM model to classic techniques such as Support Vector Regression (SVR) and Auto-Regressive Integrated Moving Average (ARIMA). In the case of poor countries, our research shows that LSTM outperforms SVR and ARIMA models, giving greater accuracy with reduced error rates
Social Entrepreneurship for Community Development: The Role of Social Capital in Establishing Sustainable Enterprises
Get PDFSocial entrepreneurship plays a crucial role in addressing social and environmental challenges, particularly in economically disadvantaged communities. This study investigates how structural, relational, and cognitive dimensions of social capital contribute to the success and sustainability of social entrepreneurship in community development. Drawing on data from sixteen in-depth interviews and 254 survey responses from social entrepreneurs, community members, and professionals in Oman, the research examines the ways in which these dimensions of social capital support sustainable enterprise development and foster community growth. The findings highlight that trust, social networks, and shared norms are essential components of social capital, enabling social entrepreneurs to access critical resources, knowledge, and networks. Furthermore, the study reveals the mediating role of sustainable enterprise development in the relationship between social capital and community outcomes, demonstrating that social capital must be leveraged through sustainable business models to achieve long-term impact. This research offers theoretical advancements by integrating social capital dimensions with social entrepreneurship, providing context-specific insights from a developing country setting. The study also provides practical recommendations for policymakers, practitioners, and social entrepreneurs, emphasizing strategies to enhance social capital and support sustainable enterprises.
Keywords: Social Entrepreneurship; Community Development; Social Capital; Sustainable Enterprise Development; Developing Countrie
Algorithmic Optimization for Efficient Air Quality Prediction Models through Machine Learning: A Case Study of Shillong City in India
No full textPoor air quality is a severe problem, potentially causing health and environmental problems. The study investigated the effectiveness of machine learning methods in air quality prediction, using the datasets from Shillong City in India. Key variables used in this study were particulate matter (PM2.5 and PM10), carbon monoxide (CO), nitrogen dioxide (NO2), nitric oxide (NO), sulphur dioxide (SO2), ammonia (NH3), and ozone (O3). The machine learning methods used in this study were AdaBoost, LightGBM and Support Vector Machine. Correlation analysis of variables with the Air Quality Index (AQI) revealed that O3 and NO had weak correlations with the AQI (r = 0.37 and 0.01, respectively), which justified their exclusion from model construction. The dataset was divided into training (70%) and testing (30%) subsets. LightGBM outperformed other models, achieving an accuracy of 0.86, whilst the AdaBoost had the second-best results. The Support Vector Machine had an accuracy of 0.827. This illustrates the usefulness of the LightGBM method in predicting air quality, providing knowledge for efficient air quality management. PM2.5 and PM10 had the greatest impact on LightGBM model results, and this illustrates the usefulness of monitoring these two air pollutants in air quality management. The LightGBM model proved to be very efficacious in predicting air quality in the given environment, considering the complex nature of the data. The model results may inform interventions for air pollution management in similar areas. Policies and regulations should therefore pay greater attention to sources of particulate matter pollution in Shillong City and develop appropriate interventions
The role of artificial intelligence in blood-borne virus opt-out testing in emergency departments
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Blood-borne viruses (BBVs) such as HIV, hepatitis B, and hepatitis C continue to pose serious public health concerns, particularly within emergency departments (EDs), where patient volume and turnover are high. While opt-out testing strategies, where individuals are tested unless they specifically decline, have shown effectiveness in increasing diagnosis rates, their adoption in EDs is limited by challenges such as inefficient workflows, data fragmentation, and suboptimal patient engagement.
Aim
This narrative review aims to explore the application of Artificial Intelligence (AI) in enhancing BBV opt-out testing in EDs, focusing on how AI can address current operational and clinical challenges while supporting ethical and equitable implementation.
Method
A structured narrative review approach was used following established guidelines. We searched PubMed, EMBASE, Web of Science, and grey literature from 2010 to 2024 using terms related to AI, blood-borne viruses, opt-out testing, and emergency departments. A total of 32 articles were included in the final synthesis.
Results
AI demonstrates theoretical potential with limited BBV-specific empirical evidence in improving BBV testing outcomes through automated patient identification and risk stratification using electronic health records. Evidence from broader healthcare AI applications suggests workflow improvements may be possible through automated test ordering, real-time alerts, and adaptive scheduling systems. Data analysis tools have shown promise in other healthcare contexts for accurate test result interpretation and epidemiological trend identification. AI-driven patient communication tools such as chatbots and mobile apps show potential to enhance patient understanding and reduce opt-out rates. Follow-up and continuity of care could potentially be strengthened via automated notifications and predictive adherence models.
Conclusion
AI offers potential opportunities to improve the scalability, efficiency, and equity of BBV opt-out testing in EDs. However, successful integration depends on addressing ethical issues, algorithmic bias, and system interoperability, supported by interdisciplinary collaboration and continuous evaluation. Further research with BBV-specific evidence is urgently needed to validate these theoretical applications
Exploring Cybersecurity Threats to Solo Female Travelers
No full textThe rise of solo female travel and increased reliance on digital tools for planning, booking, and activities highlight the significance of cybersecurity. However, this topic remains underexplored in tourism research, particularly for vulnerable solo female travelers. This study addresses the gap by examining the cybersecurity risk perceptions of solo female travelers, employing Protection Motivation Theory and conducting interviews with 26 solo female travelers worldwide. Findings reveal a remarkable shift in awareness, with participants moving from underestimating risks to adopting proactive measures. Commonly cited concerns include data breaches, identity theft, phishing, ransomware extortion, cyberstalking, and sexual harassment. Travelers expressed heightened susceptibility (threat appraisal) and a commitment to mitigation strategies (coping appraisal). They emphasized the importance of digital literacy, secure travel applications, and robust safety protocols. This study proposes that tourism businesses and destination managers institutionalize cybersecurity strategies that protect and empower digitally vulnerable solo female travelers, fostering trust and resilience
A History of Modern Britain in 12 Crises
No full textThis book provides an accessible and engaging introduction to British political history, by exploring 12 key crises from the early 20th century to the modern day. Taking each crisis in turn, chapters investigate the following crucial questions:
•What’s at stake in the crisis?
•Who was able to frame and shape the debates and narratives around the crisis?
•How was the crisis eventually resolved?
Developing a nuanced account of crisis, the book shows how narrations of events as crises can be used to promote particular political changes. Taken together the crises will familiarise readers with key themes in British politics and the events that have shaped the current shape of politics within the UK