GA-NIFS: ISM properties and metal enrichment in a merger-driven starburst during the epoch of reionization probed with JWST and ALMA

We present deep JWST/NIRSpec integral-field spectroscopy (IFS) and ALMA [C ii]158m observations of COS-3018, a star-forming galaxy at z 6.85, as part of the GA-NIFS programme. Both G395H (R 2700) and PRISM (R 100) NIRSpec observations revealed that COS-3018 is comprised of three separate components detected in [O iii]5007, which we dub as Main, North, and East, with stellar masses of 10, 10, 10 . We detect [O iii]5007,4959, [O ii]3727,3729, and multiple Balmer lines in all three components together with [O iii]4363 in the Main and North components. This allows us to measure an interstellar medium temperature of = 1.27 and = 1.6 K with densities of = 1250250 and = 700200 cm, respectively. These deep observations allow us to measure an average metallicity of 12 + log(O/H) = 7.9–8.2 for the three components with the T-method. We do not find any significant evidence of metallicity gradients between the components. Furthermore, we also detect [N ii]6585, one of the highest redshift detections of this emission line. We find that in a small, metal-poor clump 0.2 arcsec west of the North component, N/O is elevated compared to other regions, indicating that nitrogen enrichment originates from smaller substructures, possibly proto-globular clusters. [O iii]5007 kinematics show that this system is merging, which is probably driving the ongoing, luminous starburst

Increasing citizen scientist accuracy with artificial intelligence on UK camera‐trap data

As camera traps have become more widely used, extracting information from images at the pace they are acquired has become challenging, resulting in backlogs that delay the communication of results and the use of data for conservation and management. To ameliorate this, artificial intelligence (AI), crowdsourcing to citizen scientists and combined approaches have surfaced as solutions. Using data from the UK mammal monitoring initiative MammalWeb, we assess the accuracies of classifications from registered citizen scientists, anonymous participants and a convolutional neural network (CNN). The engagement of anonymous volunteers was facilitated by the strategic placement of MammalWeb interfaces in a natural history museum with high footfall related to the ‘Dippy on Tour’ exhibition. The accuracy of anonymous volunteer classifications gathered through public interfaces has not been reported previously, and here we consider this form of citizen science in the context of alternative forms of data acquisition. While AI models have performed well at species identification in bespoke settings, here we report model performance on a dataset for which the model in question was not explicitly trained. We also consider combining AI output with that of human volunteers to demonstrate combined workflows that produce high accuracy predictions. We find the consensus of registered users has greater overall accuracy (97%) than the consensus from anonymous contributors (71%); AI accuracy lies in between (78%). A combined approach between registered citizen scientists and AI output provides an overall accuracy of 96%. Further, when the contributions of anonymous citizen scientists are concordant with AI output, 98% accuracy can be achieved. The generality of this last finding merits further investigation, given the potential to gather classifications much more rapidly if public displays are placed in areas of high footfall. We suggest that combined approaches to image classification are optimal when the minimisation of classification errors is desired

Enhanced prognostic reliability for rotating machinery using neural networks with multi-scale vibration feature learning and uncertainty quantification

Reliable remaining useful life (RUL) prediction contributes to fault analysis and preventive maintenance of rotating machinery. Existing artificial intelligence methodologies, however, are challenged by inaccurate feature extraction and uncertainty involved in the RUL prediction process. To this end, this paper proposes a reliable fault prognosis method for rotating machinery using neural networks with multi-scale vibration feature learning and uncertainty quantification. Specifically, the proposed fault prognosis framework starts with constructing a multi-scale semantic embedding module to identify the semantic information in mechanical vibrations. A neural network with local and global feature extraction capabilities is then created to capture information from each scale for RUL prediction. By quantifying the uncertainty of predictions, the framework provides a confidence level for each prediction, and therefore a confidence-based RUL decision fusion method is proposed to achieve the reliable RUL estimation. The feasibility, reliability, and superiority of the framework over state-of-the-art methods are validated by datasets from machinery. Overall, the proposed framework contributes to the safe operation and maintenance of rotating machinery systems

A qualitative study exploring experiences of treatment in paediatric rheumatology - children’s, young people’s, parents’ and carers’ perspectives

Background: There is limited literature in paediatric rheumatology describing holistic lived experiences of medical treatment from perspectives of children and young people (CYP) and their parents or carers (PC). This is important as it could have implications for adherence. This study aimed to explore treatment experiences of CYP and PC in a paediatric rheumatology service. Methods: Participants were recruited at a day-case unit for intravenous infusions at a tertiary paediatric rheumatology centre. Joint qualitative semi-structured interviews with CYP and PC were used to collect data. Data were transcribed, quality checked and thematically analysed using NVivo 12.4 to identify findings. Results: Thirty-two participants (15 CYP between the ages of 6 and 16 years, 17 PC) took part in interviews lasting 41 min and 43 s, on average. Participants described experiences using infliximab, followed by tocilizumab and abatacept. Participants experienced a wave, oscillating between positive and negative trajectories. Experiences of medical treatments were described as temporary, eventually changing and leading to treatment changes or cessation. Behaviours were influenced through somatic factors (pain, function), social factors (advice from health professionals, encouragement from friends, family and teachers, practicality of using treatment in relation to school, work and finance) and cognitive factors (fear of needles, fear of specific medications, beliefs about necessity). Conclusions: Collectively, findings demonstrate experiences of medical treatment reflect the nature of many paediatric rheumatology conditions, oscillating between periods of positive and negative trajectories. Somatic, social and cognitive experiences can be positive, when treatment is considered ‘successful’. Negative somatic, social or cognitive experiences led to behaviours such as treatment non-adherence. A limitation of the study is interviews were conducted jointly with CYP and PC, which may have influenced what participants were willing to say in front of one another however this does mean findings relate to both CYP and PC and so could be suitable targets for interventions to improve adherence

An active machine learning framework for automatic boxing punch recognition and classification using upper limb kinematics

Boxing punch type classification and kinematic analysis are essential for coaches and athletes, providing critical insights into punch variety and effectiveness, which are vital for performance improvement. Existing methods for punch recognition and classification typically rely on wearable sensor data or video data; however, no fully automated system currently exists. While coaches prefer video-based analysis for its ability to easily visualize punch action errors and refine technique, video-based classification suffers from lower accuracy compared to sensor-based methods due to limitations such as motion blur. Current classification approaches typically employ supervised learning, requiring experts to annotate 70–80% of the data for model training. However, the high sampling frequency of sensor data makes this process time-consuming and challenging, leading to potential fatigue and an increased risk of inconsistent annotations by domain experts. This paper proposes a novel multimodal approach that integrates wearable sensor data and video data for automatic punch recognition and classification. The method also includes automatic segmentation of punch videos, which improves classification accuracy by utilizing both data sources. To reduce labeling effort, we apply a Query by Committee-based active learning technique, significantly decreasing the required labeling effort by one-sixth. Using only 15% of the typical labeling effort, our system achieves 91.41% accuracy for rear-hand punch recognition, 91.91% for lead-hand punch recognition, and 92.33% and 94.56% for punch classification, respectively. This Smart Boxer system aims to enhance punch analytics in boxing, providing valuable insights to improve training, optimize performance, and increase fan engagement with the sport

Systems Thinking in Mental Health Patient Safety: A Narrative Review of Complex Adaptive Systems

Despite the growth of knowledge and interest into safety and quality in healthcare more generally, the exploration in mental healthcare has been deemed to be in a narrow isolated ‘world of its own’. It is possible that relatively little attention is being paid to the processes and interdependencies within the mental health patient safety system. This may result in simplistic static measures of what the system/organisation has, not what it does (or doesn't do). This can limit the potential for learning and affecting change. To investigate systems thinking in mental health patient safety, we conducted a narrative review into the extent of evidence streams supporting systems and complexity thinking approaches. We sourced a total of 89 reports for analysis with six themes identified. These themes included studies evaluating patient safety events that have occurred within mental healthcare, research that has investigated components of the safety system, and studies that have investigated how patient safety incidents are responded to, investigated, and learned from. The review evaluated the use of systems thinking and complexity research in patient safety, and research encapsulating patient and carer involvement. Most research has focused on the analysis of historic approaches to incident investigation and on system‐based factors of patient safety, with little attention being paid to systems and complexity thinking approaches. The relationships between components were often ignored in the non‐systemic studies sourced, with relationships between components not investigated and unknown. With policymakers recommending changes in patient safety practice through system‐based approaches, it is important that its implementation is evaluated robustly with consideration of the multiple levels of the healthcare system. Future research should aim to incorporate systems‐thinking approaches to model the safety system, and to improve our understanding of the highly interconnected technical and social entities that dynamically produce emergent behaviour across the system

“Support for my dad would have benefited me because I was the one looking after him”: A qualitative analysis of the support needs of young people exposed to Adverse Childhood Experiences

Background: Adverse childhood experiences (ACEs) are associated with negative health and wellbeing outcomes. Ensuring young people receive timely and appropriate support after experiencing ACEs could improve these outcomes. Objective: This study aimed to explore what works to support young people living with ACEs; what support do they receive, and what are the characteristics of valuable help? Participants and Setting: Young people living in Wales aged 16–18 years (n = 559) completed an online survey about their ACEs and the help they did or did not receive with these experiences. Methods: Free text responses were analysed using reflexive thematic analysis. Public involvement workshops with young people were utilised to guide the analytic process. Results: Few participants reported accessing enough support. Five themes were developed: “Help me by helping my family”, “Talking to a trusted adult is helpful… until it’s not”, “Being informed: ‘I was kept in the loop’”, “Schools and colleges as sites of support” and “Loneliness and peer support”. Conclusions: More support is needed for young people with ACEs. Young people find it helpful when their whole family is supported in times of adversity, not blamed. People who provide support should be empathic and non-judgmental. Young people would rather be spoken to about ACEs and ‘kept in the loop’ than have them treated as a taboo or sensitive subject. Experiencing ACEs can be lonely in the absence of peer support. Schools and colleges are acceptable sites of support and may be well placed to provide opportunities for peer support

Salivary Testosterone, Androstenedione and 11‐Oxygenated 19‐Carbon Concentrations Differ by Age and Sex in Children

Background: The diagnosis and management of childhood adrenal disorders is challenging. Clinical markers of hormone excess or deficiency may take months to manifest, and traditional biomarkers correlate only partially with clinical outcomes. Recent work has indicated that 11 oxygenated 19‐carbon (11oxC19) steroids may be useful in the assessment of adrenal function. 11oxC19 steroids, testosterone (T) and androstenedione (A4), can be measured in saliva, but very little is known about these hormones in healthy children. Methods: Participants collected saliva samples 30 min after waking and every 2 h until bedtime. Samples were analysed for T, A4, 11 ketotestosterone (11KT) and 11βhydroxyandrostenedione (11OHA4) by liquid chromatography tandem mass spectrometry. Results: Fifty‐two (30 male) healthy children aged 10.4 ± 3.9 (5.0–17.5) participated. Median height SDS was 0.4 (IQR −0.3 to 1.01) and median BMI SDS was 0.3 (IQR −0.2 to 1.3). All steroids showed a diurnal rhythm, with all hormones decreasing in measured concentration at time points that are 30 min after waking. Salivary T was higher in postpubertal children, particularly boys (p < 0.001). Salivary A4 was lower in boys compared to girls (p = 0.009) and did not differ with pubertal development. 11KT increased with age (p < 0.001) and concentrations were similar between boys and girls. 11OHA4 reduced in concentration with age (p = 0.03) and was below detectable limits after the early morning peak in both sexes. Conclusion: For the first time we describe the physiological profile of 11KT and 11OHA4 in children. Further data are required to establish reference ranges, which should consider age, sex, pubertal status and time of sampling

Cloud-scale Gas Properties, Depletion Times, and Star Formation Efficiency per Freefall Time in PHANGS–ALMA

We compare measurements of star formation efficiency to cloud-scale gas properties across the PHANGS– ALMA sample. Dividing 67 galaxies into 1.5 kpc scale regions, we calculate the molecular gas depletion time τdepmol=Σmol/ΣSFR and the star formation efficiency per freefall time ϵffmol=τff/τdepmol for each region. Then we test how τdepmol and ϵffmol vary as functions of the regional mass-weighted mean molecular gas properties on cloud scales (60–150 pc): gas surface density, 〈Σmolcloud〉 , velocity dispersion, 〈σmolcloud〉 , virial parameter, 〈αvircloud〉 , and gravitational freefall time, 〈τffcloud〉 . 〈τffcloud〉 and τdepmol correlate positively, consistent with the expectation that gas density plays a key role in setting the rate of star formation. Our fiducial measurements suggest τdepmol∝〈τffcloud〉0.5 and ϵffmol≈0.34% , though the exact numbers depend on the adopted fitting methods. We also observe anticorrelations between τdepmol and 〈Σmolcloud〉 and between τdepmol and 〈σmolcloud〉 . All three correlations may reflect the same underlying link between density and star formation efficiency combined with systematic variations in the degree to which self-gravity binds molecular gas in galaxies. We highlight the τdepmol – 〈σmolcloud〉 relation because of the lower degree of correlation between the axes. Contrary to theoretical expectations, we observe an anticorrelation between τdepmol and 〈αvircloud〉 and no significant correlation between ϵffmol and 〈αvircloud〉 . Our results depend sensitively on the adopted CO-to-H2 conversion factor, with corrections for excitation and emissivity effects in inner galaxies playing an important role. We emphasize that our simple methodology and clean selection allow for easy comparison to numerical simulations and highlight this as a logical next direction

High Optical-to-X-Ray Polarization Ratio Reveals Compton Scattering in BL Lacertae’s Jet

Blazars, supermassive black hole systems with highly relativistic jets aligned with the line of sight, are the most powerful long-lived emitters of electromagnetic emission in the Universe. We report here on a radio-to-gamma-ray multiwavelength campaign on the blazar BL Lacertae with unprecedented polarimetric coverage from radio to X-ray wavelengths. The observations caught an extraordinary event on 2023 November 10–18, when the degree of linear polarization of optical synchrotron radiation reached a record value of 47.5%. In stark contrast, the Imaging X-ray Polarimetry Explorer found that the X-ray (Compton scattering or hadron-induced) emission was polarized at less than 7.4% (3σ confidence level). We argue here that this observational result rules out a hadronic origin of the high-energy emission and strongly favors a leptonic (Compton scattering) origin, thereby breaking the degeneracy between hadronic and leptonic emission models for BL Lacertae and demonstrating the power of multiwavelength polarimetry to address this question. Furthermore, the multiwavelength flux and polarization variability, featuring an extremely prominent rise and decay of the optical polarization degree, is interpreted for the first time by the relaxation of a magnetic “spring” embedded in the newly injected plasma. This suggests that the plasma jet can maintain a predominant toroidal magnetic field component parsecs away from the central engine