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Leveraging artificial intelligence for inclusive maternity care: enhancing access for mothers with disabilities in Africa
Full text linkWomen with disabilities face significant barriers in accessing maternal healthcare, which increases their risk of adverse pregnancy outcomes, particularly in Africa, where resources are limited. Artificial intelligence (AI) presents a unique opportunity to improve inclusivity and accessibility to antenatal care, skilled birth attendance and postnatal care for these women. This paper explores the potential of AI to address the socio-economic, physical, and institutional barriers that limit the utilisation of maternal healthcare services by women with disabilities. AI-driven technologies, such as virtual assistants, predictive analytics, and wearable devices, can enhance maternal health outcomes by improving monitoring during pregnancy, providing real-time health data, and facilitating access to skilled care. However, the successful implementation of AI in maternal healthcare in Africa faces challenges, including technological infrastructure, data quality, and ethical concerns. Collaborative efforts between governments, healthcare providers, and AI developers are necessary to overcome these challenges and ensure AI tools are inclusive, culturally sensitive, and accessible. Integrating AI into maternal healthcare services could lead to improved maternal outcomes, reduce mortality rates, and promote equity for women with disabilities in Africa
Assessing the relationship between pre- and post-game interpersonal emotions in women’s football teams
Full text linkResearchers have identified that sport emotions are interpersonal and can be transferred between a team and its members. However, studies examining the transfer of emotions across different phases of competition are limited. Consequently, the present study examined the cross-sectional, autoregressive (stability), and cross-lagged (bidirectional) relationships between collective and group-based emotions over three consecutive football matches, whilst controlling for the performance outcome. Competitive female football players (N = 47, Mage = 20.06 years; SD = 1.67) completed a sport emotion questionnaire before and immediately after a match for three consecutive games. Players also completed a perfectionism towards teammates questionnaire one week prior to data collection at football matches. Bayesian dynamic structural equation modeling revealed that collective emotions were associated with group-based emotions pre-game, but this was the case only for positive emotions. In addition, perfectionism towards one’s teammates was associated with group-based emotions at pre-game assessment. Emotions experienced at pre-game assessment were relatively stable at post-game assessment. Finally, collective emotions at pre-game assessment predicted group-based emotions at post-game assessment. It would appear that while the performance outcome strongly shapes players’ group-based emotions following football matches, pre-game collective emotions may offer earlier indications of the likely intensity of an individual’s group-based emotional response post-game; particularly when those emotions are negative
Frame Aggregation with Simple Block Acknowledgement Mechanism to Provide Strict Quality of Service Guarantee to Emergency Traffic in Wireless Networks
Full text linkThis paper proposes a frame aggregation with a simple block acknowledgement (FASBA) mechanism to provide a strict QoS guarantee to life-saving emergency traffic in wireless local area networks. This work builds on our previous work on a multi-preemptive enhanced distributed channel access protocol called MP-EDCA. The main difference between FASBA and MP-EDCA is that MP-EDCA does not provide a strict QoS guarantee to life-saving emergency traffic (e.g., ambulance calls), especially in high-load conditions. Our proposed FASBA protocol solves the problems of achieving a strict QoS guarantee to life-saving emergency traffic. The strict QoS guarantee is achieved by aggregating multiple frames with a two-bit block acknowledgement for transmissions. FASBA assures guaranteed network services by reducing MAC overheads; consequently, it offers higher throughput, lower packet delays, and accommodates a larger number of life-saving emergency nodes during emergencies. The performance of the proposed FASBA is validated by Riverbed Modeler and MATLAB 2024a-based simulation. Results obtained show that the proposed FASBA offers about 30% lower delays, 17% higher throughput, and 60% lower retransmission attempts than MP-EDCA under high-traffic loads.
Keywords: frame aggregation; block acknowledgment; QoS; MP-EDCA; 802.11e MA
Work-Related Psychological Wellbeing of Catholic Priests in Portugal: Cross-Cultural Adaptation of the Francis Burnout Inventory.
Full text linkThe present study was designed to translate the Francis Burnout Inventory Revised into Portuguese and to test this translation among a snowball sample of 266 Catholic priests serving in Portugal (91% diocesan). The data demonstrated: good internal consistency reliability for the two scales proposed by this instrument (negative affect, α = .89 and positive affect, α = .89); support for the association with a measure of self-compassion; and support for the theory of balanced affect against a measure of thoughts of leaving ministry. The priests were found to display a high level of positive affect that masked a degree of negative affect, with a third of them reporting that fatigue and irritation were part of their daily experience. [Abstract copyright: © 2025. The Author(s).
Toward net-zero in space exploration: A review of technological and policy pathways for sustainable space activities.
Full text linkSpace exploration's environmental impact presents a critical challenge to global net-zero objectives, particularly through launch emissions, orbital debris accumulation, and energy-intensive manufacturing processes. This narrative review examines technological and policy pathways toward sustainable space activities, analyzing emerging green propulsion systems, renewable energy integration, and circular economy applications in spacecraft design. The review evaluates the efficacy of current sustainability initiatives, including hydroxyl-free hydrazine propulsion, solar-electric systems, and advanced satellite technologies for environmental monitoring. Critical assessment of regulatory frameworks reveals gaps in international governance, highlighting the need for standardized carbon accounting and emissions trading schemes in space operations. The analysis extends to public-private research and development partnerships (PPRDPs), examining their role in accelerating sustainable innovation through information spillover effects and agglomeration externalities. While technological advancements demonstrate promise, particularly in reusable launch systems and space-based solar power (SBSP), significant challenges persist in deep-space mission sustainability, regulatory enforcement, and cost barriers to green technology adoption. This review synthesizes current progress and limitations in sustainable space exploration, providing insights for policymakers, industry stakeholders, and researchers working toward net-zero space operations. The findings emphasize the necessity of harmonizing space exploration objectives with environmental preservation through integrated technological innovation and international cooperation frameworks. [Abstract copyright: Copyright © 2025 The Authors. Published by Elsevier B.V. All rights reserved.
Academic writing retreats for healthcare professionals: development of the Oxford WRITE toolkit
No full textBackground Academic writing retreats offer nurses, midwives and allied health professionals (NMAHPs) opportunities to engage in research, enhance their research capabilities and produce publishable outputs. However, there are deterrents to running retreats, including time-consuming administrative loads. A toolkit to facilitate the organisation of retreats may therefore increase their number and quality by reducing administrative burdens.
Aim To report on the development and piloting of the Oxford Writing Retreat Implementation Tools for Excellence (WRITE) toolkit.
Discussion The authors created a toolkit to facilitate the organisation of high-quality writing retreats for NMAHPs. They evaluated WRITE using a pilot and interviews with the pilot retreat’s organisers. Their findings indicate the toolkit is comprehensive, user-friendly and adaptable to local contexts. It also reduces the time and resources required to organise writing retreats, which gives NMAHPs more frequent writing opportunities. They also found toolkit components needed clearer labelling and application forms for attendees should be more detailed.
Conclusion WRITE is a valuable resource for organising high-quality, reproducible, writing retreats for NMAHPs. Its availability and adaptability make it suitable for widespread implementation internationally.
Implications for practice WRITE facilitates the efficient organisation of writing retreats, potentially increasing their frequency and accessibility. Its user-friendliness may lower barriers to retreat organisation, expanding opportunities for NMAHPs to engage in academic writing activities. This may lead to increased research outputs and scholarly contributions from this healthcare demographic
Understanding the Behaviors and Detecting the Mental Health Symptoms Among Students Using Machine Learning Algorithms
No full textInternational students studying in the UK are facing challenges in their life that can impact their mental health. To better understand and address these challenges, this research paper investigates the behaviors of the students and the potential machine learning algorithms to predict the mental health of them. A form was distributed to international students at various universities in the UK. The survey aimed to gather information on aspects like age, country of origin, sleep patterns, emotional behaviors, support from family and the university, financial pressures, experiences of loneliness, and living standards, all in an effort to gain insights into their mental well-being. The collected data has been cleaned, pre-processed, and visualized the findings such as whether male or female are affected much based on the collected data, their sleeping behavior, financial stress, and so on. Later, machine learning algorithms such as decision tree, CatBoost, random forest, and XGBoost classifiers were used to predict the mental health status of the students, and the random forest works best with 88% accuracy with the highest precision of 0.92 and F1-score of 0.82. The findings from this research have the potential to offer valuable perspectives and assistance in improving mental health services for international students in the UK. By using machine learning algorithms, this study aims to improve upon traditional methods of mental health prediction and provide a more efficient and accurate means of identifying and supporting students in need
