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Antidepressant and anxiolytic medications and risk of mortality in people with dementia: a nested case-control study in Northern Ireland
No full textBackground Antidepressant and anxiolytic medication use in people with dementia (PwD) may contribute to potentially inappropriate prescribing and be associated with mortality.Objective To investigate trends in prescribing of these medications and their association with mortality risk among PwD.Methods A nested case-control study was conducted in Northern Ireland (NI) using linkage of five administrative population-based data sources within a cohort of dementia patients (identified if a medication indicated for dementia was prescribed). Dementia patients who died were matched to one control who lived at least as long as their matched case after dementia diagnosis (matched on age, sex and year of dementia). Exposure to antidepressant and anxiolytic medications was assessed from two years prior to study entry. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated using conditional logistic regression after adjusting for demographic factors and comorbidities. Results The study included 14,420 dementia cases. Antidepressants were prescribed to 59.2% of cases and 54.7% of controls whilst 44.8% of cases and 36.0% of controls were prescribed anxiolytics. There was evidence of a weak increased risk of mortality in PwD prescribed antidepressants (fully adjusted OR=1.08; 95% CI 1.02-1.14) and a strong increased risk in those prescribed anxiolytics (fully adjusted OR=1.26; 95% CI 1.19-1.33) compared to nonusers. Conclusions In this large NI population-based cohort of PwD, elevated levels of antidepressant and anxiolytic prescribing were observed. The use of anxiolytic medications was strongly associated with mortality in PwD
Evaluating the effects of the crescendo programme on music and self-regulation with 5–6-year-old pupils: a quasi-experimental study
No full textCrescendo is a music-based social and emotional learning (SEL) programme designed for primary/elementary school children living in disadvantaged communities. It is a community-led, orchestra-delivered, and evidence-informed initiative aimed at im-proving children’s musical and SEL outcomes through sustained engagement. Children growing up in socioeconomically disadvantaged areas often experience challenges with SEL and limited access to orchestral music education. However, emerging research suggests a relationship between music participation and SEL development. This study evaluated the initial impact of Crescendo on 559 children aged 5–6 in their first year of participation (Year 1 of 7). A quasi-experimental, rolling cohort design compared pupils in four participating Crescendo schools with pupils in four matched control schools not re-ceiving the programme. Outcome measures included music skills (beat, pitch, and re-action to music) and SEL (behavioural self-regulation). The findings indicated significant positive effects of the programme across all outcome domains, with moderate effects observed in self-regulation (Cohen’s d = 0.29) and reaction to music (Cohen’s d = 0.21) compared to control schools. These results suggest that collaboration between orchestral musicians and educators can positively influence young children’s musical and SEL development in resource-constrained settings. The findings also underscore the im-portance of clearly defined programme models to support replication and scalability
Early human settlement in Bornean forests occurred under distinct environmental conditions
No full textHumans have inhabited Bornean tropical forests for at least 45,000 years, yet the impact of that long‐term presence is poorly understood. Borneo's extensive archaeological record and high biodiversity offer a unique laboratory to examine patterns of site selection and the effects of human activity on tropical forest ecosystems. This paper establishes a synthetic literature‐based data set of 73 archaeological sites, 47 with radiocarbon or radiometric records dating during the last 6 ka BP. Palaeoclimate, current climate, soil, and terrain variables of these 47 sites are compared against a set of simulated random locations across Borneo. We also consider spatial proximity as a factor influencing human settlement, clustering sites based on location and examining the timing and duration of habitation of sites within the clusters. Finally, we compare the 47 site records against the HYDE‐3.2 land‐use model to assess correspondence. Our results suggest that during the last 6000 years BP, Bornean human settlements tended to be located in the forests at lower elevations, near coasts and river networks, in higher temperatures and lower precipitation. Comparative sedimentary analysis also suggests preferential use of locations exhibiting lower clay and higher sand content. This combination of conditions likely improved food production and resource supply. Our study highlights the value of integrating archaeological data with global historical land‐use and climate models to uncover long‐term human–environment interactions. By establishing a cross‐site environmental baseline, these findings provide insights into past human settlement patterns and likely human legacies in Borneo's tropical forests
Spirituality and religion and the role in improving teaching approaches to diversity and inclusion in the nursing and midwifery curriculum: an explanatory sequential multi-methods study
No full textBackgroundSpirituality and religion play an important role in many people’s lives. While healthcare professionals support people from a diverse range of backgrounds, cultures and belief systems, these dimensions are often missing from assessments and care plans. Using the lens of nursing and midwifery students and academic teaching staff, this study sought to explore how the concepts of spirituality and religion could be better incorporated into nursing and midwifery teaching programmes, while acting as a possible conduit for exploring the richness of diversity and inclusion.MethodsAn explanatory sequential multi-methods study, to include an online survey (n = 114 responses) and focus groups (n = 11 participants). Quantitative data were analysed using descriptive statistics and qualitative data were analysed using reflexive thematic analysis. Integration of the quantitative and qualitative data was achieved through a pillar integration process.ResultsThe concept of spirituality was viewed as predominately positive, something personal to individuals and linked to how people make sense of their place in the world. Religion was seen as a connectedness to a community with common beliefs and a shared identity. However, the rules and regulations associated with religion, were perceived by some respondents as leading to intolerance and the exclusion of others. Overall, participants believed that greater awareness of spirituality and religion could help people to be more aware and to be more welcoming of diversity leading to greater inclusion. Participants believed that these concepts should be included in teaching programmes and integrated with clinical practice.ConclusionsStudents and clinical practitioners should be encouraged to increase their knowledge and awareness towards spiritual and religious issues. This awareness may begin with students and clinical practitioners reflecting on their own beliefs and values enabling them to be more sensitive to and respond to the beliefs and values of others. Insights gained by this study may be valuable to healthcare educators and policymakers highlighting the need for greater awareness of spirituality and religion in health and social care training.<br/
Towards real-time pork breed and boar taint classification using rapid evaporative ionisation mass spectrometry
No full textTo help counteract food fraud and meet consumer expectations, the pork industry requires reliable quality-monitoring and traceability systems. In this context, rapid evaporative ionisation mass spectrometry (REIMS) could be rolled out as a real-time, accurate metabolic fingerprint-based classifier of pork meat characteristics and quality issues, such as genetic origin and boar taint. Here, fingerprinting of >3000 pig neck fat samples enabled highly accurate pig breed classification (pairwise comparison of Commercials (Pietrain × Hampshires × Durocs, Large-Whites, Durocs), Hampshires and Large-Whites, where data modelling using support vector machine (SVM, all pairwise comparisons > 89%) and orthogonal partial least squares-discriminant analysis (OPLS-DA, >90%) outperformed random forest (RF, 72.0–79.5%). Boar taint classification showed comparable results between OPLS-DA, RF and SVM (93.5–96.0%), but it was important to apply strategies to avoid false negatives and positives, including the construction of balanced models (tainted vs. non-tainted)
Life cycle assessment of a wave cycloidal rotor: environmental performance and improvement pathways
No full textWave energy technology needs to be reliable, efficient, and environmentally sustainable. Therefore, life cycle assessment (LCA) is a critical tool in the design of marine renewable energy devices. However, LCA studies of floating type wave cycloidal rotors remain limited. This study builds on previous work by assessing the cradle-to-grave environmental impacts of a cycloidal rotor wave farm, incorporating updated material inventories, site-dependent energy production, and lifetime extension scenarios. The farm with the steel cyclorotor configuration exhibits a carbon intensity of 21.4 g CO2 eq/kWh and an energy intensity of 344 kJ/kWh, which makes it a competitive technology compared to other wave energy converters. Alternative materials, such as aluminium and carbon fibre, yield mass reductions but incur higher embodied emissions. Site deployment strongly influences performance, with global warming potential reduced by up to 50% in high-power-density sites, while extending the operational lifetime from 25 to 30 years further reduces the impact by 17%. Overall, the results highlight the competitive environmental performance of floating wave cycloidal rotors and emphasize the importance of material selection, site selection, and lifetime extension strategies in reducing life cycle impacts
Integrating probabilistic trees and causal networks for clinical and epidemiological data
No full textHealthcare decision-making requires not only accurate predictions but also insights into how factors influence patient outcomes. While traditional machine learning (ML) models excel at predicting outcomes, such as identifying high-risk patients, they are limited in addressing “what if” questions about interventions. This study introduces the Probabilistic Causal Fusion (PCF) framework, which integrates Causal Bayesian Networks (CBNs) and Probability Trees (PTrees) to extend beyond predictions. PCF leverages causal relationships from CBNs to structure PTrees, enabling both the quantification of factor impacts and the simulation of hypothetical interventions. The framework is evaluated on three clinically diverse, real-world datasets, MIMIC-IV, Framingham Heart Study, and BRFSS (Diabetes), demonstrating consistent predictive performance comparable to conventional ML models, while offering enhanced interpretability and causal reasoning capabilities. In contrast to conventional approaches focused solely on prediction, PCF offers a unified framework for prediction, intervention modelling, and counterfactual analysis, forming a holistic toolkit for clinical decision support. To enhance interpretability, PCF incorporates sensitivity analysis and SHapley Additive exPlanations (SHAP). Sensitivity analysis quantifies the influence of causal parameters on outcomes such as Length of Stay (LOS), Coronary Heart Disease (CHD), and Diabetes, while SHAP highlights the importance of individual features in predictive modelling. This dual-layered interpretability offers both macro-level insights into causal pathways and micro-level explanations for individual predictions. By combining causal reasoning with predictive modelling, PCF bridges the gap between clinical intuition and data-driven insights. Its ability to uncover relationships between modifiable factors and simulate hypothetical scenarios provides clinicians with a clearer understanding of causal pathways. This approach supports more informed, evidence-based decision-making, offering a robust framework for addressing complex questions in diverse healthcare settings
Error performance characterization of LoRa-based direct-to-satellite IoT
No full textRecently, Long-Range (LoRa)-based direct-to-satellite Internet-of-Things (DtS-IoT) has garnered widespread attention from both academia and industry due to its capability to provide pervasive connectivity in an energy-efficient and cost-effective manner. A rigorous error performance analysis of such a new paradigm is quite essential for future green IoT communications. In this paper, we provide a novel analytical framework to characterize the error performance of LoRa-based DtS-IoT systems by leveraging an empirically-verified satellite-to-ground channel model. To enable a practical performance analysis, non-coherent detection is considered in the presence of interference along with the relative time and frequency offsets, where the corresponding decision metrics are theoretically derived. Based on this, closed-form symbol and bit error rate expressions are obtained by approximating the impact of the overall interference distributed within the decision metrics by that of the peak interference. Moreover, the impact of some key system parameters, such as the spreading factor (SF), bandwidth, and the end-device’s (ED’s) location, on the error performance is thoroughly investigated. The validity of our theoretical analysis is substantiated by extensive numerical simulations, where further insights are obtained into the error performance improvements of LoRa-based DtS-IoT systems
Eruption-related ultraviolet irradiance enhancements associated with flares
No full textLarge solar flares (GOES M-class or higher) are usually associated with eruptions of material. However, when considering flare irradiance enhancements and dynamics such as chromospheric evaporation, potential contributions from erupted material have historically been neglected. We analyse nine eruptive M- and X-class flares from 2024 to early 2025, quantifying the relative contributions of erupted material to irradiance enhancements during the events. Atmospheric Imaging Assembly (AIA) images from four different channels had ribbon and eruption irradiance contributions separated using a semi-automated masking method. The sample-averaged percentages of excess radiated energy by erupted material over the impulsive phase were 10−4+4%, 24−14+14%, 21−10+14% and 13−9+6% for the 131 Å, 171 Å, 304 Å and 1600 Å channels, respectively. For three events that were studied in further detail, hard X-ray (HXR) imaging showed little to no signatures of nonthermal heating within the eruptions. Our results suggest that erupted material can be a significant contributor to UV irradiance enhancements during flares, with possible heating mechanisms including nonthermal particle heating, Ohmic heating, or dissipation of MHD waves. Future work may clarify the heating mechanism and evaluate the impact of eruptions on spectral variability, particularly in Sun-as-a-star and stellar flare observations.<br/
MBTModelGenerator: automated reverse engineering of test models from clickstream data for model-based testing of web applications
No full textContextModel-Based Testing (MBT) was first introduced in 1970′s, and has the potential to improve efficiency and effectiveness of testing. However, its adoption—especially for web applications—has been hindered by the effort required to manually design MBT models, and keep them updated.ObjectiveBased on the above challenge in a real industrial context, this study introduces an automated approach to reduce that effort by reverse engineering MBT models from clickstream data captured during users' interaction with web applications.MethodWe have developed and present in this paper an open-source tool, named MBTModelGenerator, which logs user interactions via a lightweight JavaScript module in the front-end, and transmits them to a REST API backend. These interactions are then transformed into directly executable MBT models in the input format of an open-source MBT tool named GraphWalker.ResultsThe tool was evaluated on two representative open-source web applications, Spring PetClinic and a Task Manager web app, and is under evaluation in several large-scale industrial testing projects. The generated MBT models accurately reflected user navigation flows and could be executed in the GraphWalker MBT tool without any manual changes. Using the tool has significantly reduced the effort of MBT model design by more than 90%, while still allowing test engineers to inspect and refine the generated models for completeness.ConclusionOur approach facilitates lightweight adoption of MBT by automating model generation, which is the most effort intensive phase of MBT. To ensure correctness and completeness, the generated models should still be reviewed by test engineers —but that effort remains substantially lower than designing MBT models from scratch. The tool is in active industrial use and available as open-source for reuse and further development.<br/