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Full-Scale Fire Testing to Assess the Risk of Battery Electric Vehicle Fires in Underground Car Parks
Get PDFBattery-only electric vehicle (BEV) fires have recently emerged as a significant safety concern in modern society, particularly when they occur in car parking spaces, where the consequences can be severe. While relatively few fatalities and injuries are reported, the associated economic losses are substantial. Car park structures present several challenges from a fire safety perspective, including high energy content, confined geometry, and difficulties in detecting and accessing to the fire’s origin due to smoke accumulation in spaces with limited ventilation and exits. This study investigates this fire hazard by conducting a full-scale fire test on a modern BEV in an instrumented test rig that simulates a segment of an underground car park. The data obtained were compared with standard fire curves to assess the hazard’s characteristics and with the data from a companion study to identify differences between BEV fires in underground and surface car parks. The primary difference observed was deflagration venting, which occurred shortly after the initial ignition of combustible gases accumulated beneath the ceiling, lasting until 13 min and 5 s. This phenomenon led to a rapid initial growth of the BEV fire, which burned more intensely for a shorter duration in the semi-enclosed configuration comparted to the open configuration. The enclosed fire recorded an average ceiling-jet temperature of approximately 1100°C and a peak incident heat flux exceeding 225 kW/m2. Additionally, thermal runaway within the lithium-ion battery cells was analysed to understand the adverse effects of the enclosed environment on thermal runaway propagation. This fire testing provides critical insights for developing firefighting strategies including the proper preparation of equipment and the design of fire protection systems. The principal findings could inform tactics for mitigating unforeseen fire risks and contribute to revisions of fire safety regulations
M.N. Pokrovskii and the Origins of Soviet Historiography
Get PDFBook Review (M.N. Pokrovskii and the Origins of Soviet HistoriographyJames D. White, Leiden, Brill, 2024, 332 pp., €129 (hardback), ISBN: 9789004703872
An international, multi-perspective survey examining the poststroke impact and unmet needs following young stroke
Get PDFPurposeResearch into young stroke survivors’ unmet needs is limited, despite this cohort accounting for ∼25% of the stroke population.MethodsThis international survey acts as the first to explore the post-stroke impact and unmet needs across three key stakeholder groups: young stroke survivors, young stroke carers and healthcare professionals. Surveys were distributed via stroke organization newsletters and support groups. Survey questions consisted of existing validated outcome measures, closed and open-ended questions. Survey responses underwent ANOVA testing and regression modelling on validated measures: Stroke Impact Scale (SIS), Adult Carer Quality of Life questionnaire (AC-QoL) and Zarit Burden Interview (ZBI).ResultsSurvey data was collected from 316 young stroke survivors, 68 young stroke carers and 117 healthcare professionals. Young stroke survivors’ mean (SD) SIS score was 67.15 (25.17) and carers reported AC-QoL scores and ZBI scores of 70.16 (21.10) and 30.47 (19.20) respectively, indicating mid-range quality of life and burden. Common themes that arose in qualitative accounts highlighted impacts and unmet needs in psychosocial, occupational and quality of life support.ConclusionsParticipants reported a range of post-stroke impacts and unmet needs specific to a younger cohort. These should be considered when developing and providing services for young stroke survivors
“ We need more guidance, more encouragement and empowerment for what our bodies are capable of ”, pregnant and postpartum women’s knowledge and experiences of receiving physical activity guidance and support on the island of Ireland: an online survey study
Get PDFBackground: Physical activity (PA) during pregnancy provides maternal and foetal health benefits such as improving mental wellbeing, cardiometabolic and delivery outcomes. However, little is known about pregnant and postpartum individuals’ PA knowledge or guidance and support received during maternity care on the island of Ireland. This study aimed to assess knowledge of PA guidelines and explore experiences of receiving PA guidance and support during maternity care. Methods: Pregnant (≥ 8 weeks gestation, post-initial maternity appointment) or postpartum (birthed and received maternity care within three years previous) adults who received antenatal care on the island of Ireland completed an online survey. Descriptive analysis and frequencies were performed with the principles of thematic analysis applied to the concluding open-text question. Results: Of the 430 women surveyed only 7% (n = 30) correctly stated the PA guidelines for pregnancy and postpartum. 28% (n = 120) received PA advice from a healthcare practitioner (HCP) during maternity care. Overall, few felt timely (24%, n = 103) or clear and easy to follow (25%, n = 107) advice was received. 22% (n = 96) felt confident in the advice received and only 17% (n = 74) felt supported to engage in PA. Two themes and seven subthemes relating to women’s experiences of PA guidance and support during pregnancy and future needs were generated. Using study findings, five actionable steps were created. Conclusions: 93% of women surveyed could not accurately state the PA guidelines for pregnancy and postpartum. Largely, maternity care delivered on the island of Ireland does not include PA guidance or support. Recommendations are proposed to improve PA guidance and support provided during pregnancy and following childbirth on the island of Ireland
The respiratory microbiome in patients with post-COVID-19 residual lung abnormalities resembles that of healthy individuals and is distinct from idiopathic pulmonary fibrosis
Get PDFIntroductionUp to 11% of patients are left with residual lung abnormalities following COVID-19 infection. It is unclear whether these changes resolve over time or progress to fibrosis. The airway microbiome is altered in interstitial lung disease, potentially contributing to pathogenesis and disease progression. We hypothesised that the airway microbiome in patients with post-COVID-19 residual lung abnormalities may be altered.MethodsThe POST COVID-19 interstitial lung DiseasE (POSTCODE) study recruited subjects with post-COVID-19 residual lung abnormalities for bronchoscopy. 16S ribosomal RNA gene amplicon sequencing was performed on DNA extracted from bronchoalveolar lavage fluid and compared with that from patients with idiopathic pulmonary fibrosis, fibrotic hypersensitivity pneumonitis and control subjects.Results28 subjects with post-COVID-19 residual lung abnormalities were recruited an average of 11 months after infection. No significant associations were found between the lower airway microbiome or bacterial burden and disease severity or trajectory. There was no difference in bacterial burden between post-COVID-19 patients and interstitial lung disease or control subjects. Furthermore, no differences in microbial composition were observed between these patients and those with fibrotic hypersensitivity pneumonitis or controls. However, compared with idiopathic pulmonary fibrosis, there was an increased abundance of Streptococcus and higher α-diversity in subjects with post-COVID-19 residual lung abnormalities.ConclusionsThe microbiome and bacterial burden in the lower airways of subjects with post-COVID-19 residual lung abnormalities do not differ from those of controls. The microbiome differs from idiopathic pulmonary fibrosis. This, and the absence of associations between microbial features and disease severity or clinical outcomes, suggests that the microbiome is unlikely to contribute to residual lung abnormalities in patients recovering from COVID-19 infection
Comparison of spatial prediction models from Machine Learning of cholangiocarcinoma incidence in Thailand
Get PDFBackground: Cholangiocarcinoma (CCA) poses a significant public health challenge in Thailand, with notably high incidence rates. This study aimed to compare the performance of spatial prediction models using Machine Learning techniques to analyze the occurrence of CCA across Thailand. Methods: This retrospective cohort study analyzed CCA cases from four population-based cancer registries in Thailand, diagnosed between January 1, 2012, and December 31, 2021. The study employed Machine Learning models (Linear Regression, Random Forest, Neural Network, and Extreme Gradient Boosting (XGBoost)) to predict Age-Standardized Rates (ASR) of CCA based on spatial variables. Model performance was evaluated using Root Mean Square Error (RMSE) and R2 with 70:30 train-test validation. Results: The study included 6,379 CCA cases, with a male predominance (4,075 cases; 63.9%) and a mean age of 66.2 years (standard deviation = 11.1 years). The northeastern region accounted for most of the cases (3,898 cases; 61.1%). The overall ASR of CCA was 8.9 per 100,000 person-years (95% CI: 8.7 to 9.2), with the northeastern region showing the highest incidence (ASR = 13.4 per 100,000 person-years; 95% CI: 12.9 to 13.8). In the overall dataset, the Random Forest model demonstrated better prediction performance in both the training (R2 = 72.07%) and testing datasets (R2 = 71.66%). Regional variations in model performance were observed, with Random Forest performing best in the northern, northeastern regions, while XGBoost excelled in the central and southern regions. The most important spatial predictors for CCA were elevation and distance from water sources. Conclusion: The Random Forest model demonstrated the highest efficiency in predicting CCA incidence rates in Thailand, though predictive performance varied across regions. Spatial factors effectively predicted ASR of CCA, providing valuable insights for national-level disease surveillance and targeted public health interventions. These findings support the development of region-specific approaches for CCA control using spatial epidemiology and machine learning techniques
Instrumented Mouthguards in Men’s Rugby League: Quantifying the Incidence and Probability of Head Acceleration Events at a Group and Individual Level
Get PDFBackgroundThere is growing concern that exposure to head acceleration events (HAEs) may be associated with long-term neurological effects.ObjectivesTo quantify the incidence and probability of HAEs during men’s professional rugby league match-play on a group and individual basis using instrumented mouthguards (iMGs).MethodsA total of 91 men’s professional rugby league players participating in the 2023 Super League season wore iMGs, resulting in the collection of 775 player matches (mean 8.3 matches per player). Incidence of HAEs (rate of HAEs per median playing time) was calculated via generalised linear mixed models. Probability of HAEs (likelihood of experiencing an HAE during a tackle-event) was calculated using an ordinal mixed effects regression model.ResultsThe mean incidence of HAEs exceeding 25 g per median playing time ranged from 0.86–1.88 for back positions and 1.83–2.02 for forward positions. The probability of exceeding 25 g during a tackle event was higher for ball-carriers (6.29%, 95% confidence intervals [CI] 5.27–7.58) than tacklers (4.26%, 95% CI 3.48–5.26). Several players exhibited considerably higher incidence and probability than others, e.g. one player averaged 5.02 HAEs exceeding 25 g per median playing time and another had a probability of 20.00% of exceeding 25 g during a tackle event as a ball-carrier and 34.78% as a tackler.ConclusionsThis study quantifies the incidence and probability of HAEs in men’s rugby league match-play, advancing our understanding of HAE exposure in men’s rugby league. These findings support the development of individualised HAE mitigation strategies targeted at individuals with elevated HAE exposures
Evaluating rule-based and generative data augmentation techniques for legal document classification
Get PDFAutomated text classification is a fundamental research topic within the legal domain as it is the foundation for building many intelligent legal solutions. There is a scarcity of publicly available legal training data and these classification algorithms struggle to perform in low data scenarios. Text augmentation techniques have been proposed to enhance classifiers through artificially synthesised training data. In this paper we present and evaluate a combination of rule-based and advanced generative text augmentation methods designed to create additional training data for the task of classification of legal contracts. We introduce a repurposed CUAD contract dataset, modified for the task of document level classification, and compare a deep learning distilBERT model with an optimised support vector machine baseline for useful comparison of shallow and deep strategies. The deep learning model significantly outperformed the shallow model on the full training data (F1-score of 0.9738 compared to 0.599). We achieved promising improvements when evaluating the combined augmentation techniques on three reduced datasets. Augmentation caused the F1-score performance to increase by 66.6%, 17.5% and 2.6% for the 25%, 50% and 75% reduced datasets respectively, compared to the non-augmented baseline. We discuss the benefits augmentation can bring to low data regimes and the need to extend augmentation techniques to preserve key terms in specialised domains such as law.</p
Students' acceptance and use of generative AI in pharmacy education: international cross-sectional survey based on the extended unified theory of acceptance and use of technology
Get PDFBackground: Generative artificial intelligence (GenAI) has significant potential implications for pharmacy education, but its ethical, practical, and pedagogical implications have not been fully explored. Aim: This international study evaluated pharmacy students’ acceptance and use of GenAI tools using the Extended Unified Theory of Acceptance and Use of Technology (UTAUT). Method: A cross-sectional survey of pharmacy students from nine countries during the first half of 2024 assessed GenAI usage patterns, curricular integration, and acceptance via the Extended UTAUT framework. After appropriate translation and cultural adaptation, exploratory factor analysis (EFA) identified key adoption factors. Results: A total of 2009 responses were received. ChatGPT and Quillbot were the tools most frequently utilised. EFA identified three key dimensions: Utility-Driven Adoption, Affordability and Habitual Integration, and Social Influence. Students rated performance and effort expectancy highly, highlighting their perceived usefulness and ease of use of GenAI tools. In contrast, habit and price value received lower ratings, indicating barriers to habitual use and affordability concerns. Gender disparities were noted, with males demonstrating significantly higher acceptance (p < 0.001). Additionally, country-specific differences were evident, as Malaysia reported a high performance expectancy, while Egypt exhibited low facilitating conditions. Over 20% indicated an over-reliance on GenAI for assignments, raising ethical concerns. Significant gaps were observed, such as limited ethical awareness—only 10% prioritised legal and ethical training—and uneven curricular integration, with 60% reporting no formal exposure to Generative AI. Conclusion: Findings reveal critical gaps in ethical guidance, equitable access, and structured GenAI integration in pharmacy education. A proactive, context-specific strategy is essential to align technological innovation with pedagogical integrity.</p
Age, gender and regional/ethnic variations in emmetropic axial growth rate
Get PDFAim: To determine age‐specific axial growth rate in emmetropic eyes and investigate the effect of sex and region/ethnicity using population‐based data. Methods: A retrospective analysis of five population‐based studies conducted in the United Kingdom, Sweden and China. A total of 16,526 datapoints from 6753 participants, aged 6–16.9 years, with spherical equivalents (SE) from −0.49 to +1 D were analysed. Axial length was modelled using a Generalised Estimating Equation with region/ethnicity and sex included as fixed factors and age, SE and corneal radius of curvature as covariates. Model‐based estimates of axial length were used to derive age‐specific axial growth rates, maintaining SE at 0.00 D and constant corneal radius of curvature. Results: Within this emmetropic population, axial length was weakly correlated with SE (r = −0.24) but strongly associated with corneal curvature (r = 0.76). Gender, region/ethnicity, SE, corneal curvature and inverse function of age were associated with axial length of emmetropic eyes. Axial length was longer in males than females by 0.55 mm (95% CI: 0.53–0.56 mm) in East‐Asian emmetropic eyes and by 0.50 mm (95% CI: 0.49–0.52 mm) in European eyes; however, axial growth rate was marginally greater in males by 7%. Axial length of East‐Asian eyes was significantly greater than Europeans by 0.14 mm (95% CI: 0.12–0.16 mm) in males and 0.12 mm (95% CI: 0.11–0.14 mm) in females, but axial growth rate was not significantly different between regions/ethnicities (p = 0.06). Axial growth rate decreased non‐linearly from 0.17 to 0.03 mm/year in males and 0.16 to 0.02 mm/year in females between 6 and 16 years. Conclusions: Emmetropic axial growth rate between 6 and 16 years is non‐uniform with greater growth rate at younger ages and in males. Growth rates estimated by maintaining constant SE and corneal curvature are lower or similar to previous estimates and may be used to set goals for myopia treatment