Revealing the Nature of yrast States in Neutron-Rich Polonium Isotopes.

Polonium isotopes having two protons above the shell closure at Z=82 show a wide variety of low-lying, high-spin isomeric states across the whole chain. The structure of neutron-deficient isotopes up to ^{210}Po  (N=126) is well established as they are easily produced through various methods. However, there is not much information available for the neutron-rich counterparts for which only selective techniques can be used for their production. We report on the first fast-timing measurements of yrast states up to the 8^{+} level in ^{214,216,218}Po isotopes produced in the β^{-} decay of ^{214,216,218}Bi at ISOLDE, CERN. In particular, our new half-life value of 607(14) ps for the 8_{1}^{+} state in ^{214}Po is nearly 20 times shorter than the value available in the literature and comparable with the newly measured half-lives of 409(16) and 628(25) ps for the corresponding 8_{1}^{+} states in ^{216,218}Po, respectively. The measured B(E2;8_{1}^{+}→6_{1}^{+}) transition probability values follow an increasing trend relative to isotope mass, reaching a maximum for ^{216}Po. The increase contradicts the previous claims of isomerism for the 8^{+} yrast states in neutron-rich ^{214}Po and beyond. Together with the other measured yrast transitions, the B(E2) values provide a crucial test of the different theoretical approaches describing the underlying configurations of the yrast band. The new experimental results are compared to shell-model calculations using the KHPE and H208 effective interactions and their pairing-modified versions, showing an increase in configuration mixing when moving toward the heavier isotopes

Health and Experiences During the COVID-19 Pandemic Among Children and Young People: Analysis of Free-Text Responses From the Children and Young People With Long COVID Study.

BackgroundThe literature is equivocal as to whether the predicted negative mental health impact of the COVID-19 pandemic came to fruition. Some quantitative studies report increased emotional problems and depression; others report improved mental health and well-being. Qualitative explorations reveal heterogeneity, with themes ranging from feelings of loss to growth and development.ObjectiveThis study aims to analyze free-text responses from children and young people participating in the Children and Young People With Long COVID study to get a clearer understanding of how young people were feeling during the pandemic.MethodsA total of 8224 free-text responses from children and young people were analyzed using InfraNodus, an artificial intelligence-powered text network analysis tool, to determine the most prevalent topics. A random subsample of 411 (5%) of the 8224 responses underwent a manual sentiment analysis; this was reweighted to represent the general population of children and young people in England.ResultsExperiences fell into 6 main overlapping topical clusters: school, examination stress, mental health, emotional impact of the pandemic, social and family support, and physical health (including COVID-19 symptoms). Sentiment analysis showed that statements were largely negative (314/411, 76.4%), with a small proportion being positive (57/411, 13.9%). Those reporting negative sentiment were mostly female (227/314, 72.3%), while those reporting positive sentiment were mostly older (170/314, 54.1%). There were significant observed associations between sentiment and COVID-19 status as well as sex (P=.001 and PConclusionsWe used InfraNodus to analyze free-text responses from a large sample of children and young people. The majority of responses (314/411, 76.4%) were negative, and many of the children and young people reported experiencing distress across a range of domains related to school, social situations, and mental health. Our findings add to the literature, highlighting the importance of specific considerations for children and young people when responding to national emergencies

The CASPAR study protocol. Can cervical stiffness predict successful vaginal delivery after induction of labour? a feasibility, cohort study.

BackgroundInduction of labour (IOL) is a common obstetric intervention in the UK, affecting up to 33% of deliveries. IOL aims to achieve a vaginal delivery prior to spontaneous onset of labour to prevent harm from ongoing pregnancy complications and is known to prevent stillbirths and reduce neonatal intensive care unit admissions. However, IOL doesn't come without risk and overall, 20% of mothers having an induction will still require a caesarean section birth and in primiparous mothers this rate is even higher. There is no reliable predictive bedside tool available in clinical practice to predict which patient's undergoing the IOL process will result in a vaginal birth; the fundamental aim of the IOL process. The Bishop's Score (BS) remains in routine clinical practice as the examination tool to assess the cervix prior to IOL, despite it being proven to be ineffective as a predictive tool and largely subjective. This study will assess the use of the Pregnolia System, a new objective antenatal test of cervical stiffness. This study will explore its' potential for pre-induction cervical assessment and indication of delivery outcome following IOL.MethodsCASPAR is a feasibility study of term, primiparous women with singleton pregnancies undergoing IOL. Cervical stiffness will be assessed using the Pregnolia System; a novel, non-invasive, licensed, CE-marked, aspiration-based device proven to provide objective, quantitative cervical stiffness measurements represented as the Cervical Stiffness Index (CSI, in mbar). A measurement is obtained by applying the sterile single-use Pregnolia Probe directly to the anterior lip of the cervix, visualised via placement of a speculum. Following informed consent, CASPAR study participants will undergo the Pregnolia System cervical stiffness assessment prior to their IOL process commencing. Participant questionnaires will evaluate the acceptability of this assessment tool in this population. This study will directly compare this novel antenatal test to the current BS for both patient experience of the different cervical assessment tools and for IOL outcome prediction.DiscussionThis feasibility study will explore the use of this novel device in clinical practice for pre-induction cervical assessment and delivery outcome prediction. Our findings will provide novel data that could be instrumental in transforming clinical practice surrounding IOL. Determining recruitment rates and acceptability of this new assessment tool in this population will inform design of a further powered study using the Pregnolia System as the point-of-care, bedside cervical assessment tool within an IOL prediction model.Study registrationThis study is sponsored by The University of Liverpool and registered at ClinicalTrials.gov, identifier NCT05981469, date of registration 7th July 2023

Adaptive Kriging-assisted multi-fidelity subset simulation for reliability analysis

Accurate estimation of rare event probabilities with reasonable computational demands is crucial in reliability analysis. However, with increasing complexity of engineering problems, traditional methods are facing rising challenges in terms of computational efficiency and accuracy. In this work, an effective multi-fidelity framework is provided for assessing rare event probabilities. We firstly define the multi-fidelity failure domains by introducing a series of intermediate failure events associated with performance functions at various fidelity levels. Subset simulation is then employed to decompose the rare event probability into a series of conditional probabilities associated with these multi-fidelity failure domains. In this context, we demonstrate that the estimation accuracy of failure probability only depends on that of the conditional probability of a critical failure domain, rather than on those of the rest of multi-fidelity failure domains. With aid of this fact, the rest of failure domains is approximated by a series of Kriging models constructed with the computationally cheap low-fidelity performance functions. Thus, the computational demand for estimating the conditional probabilities of the rest failure domains is significantly decreased in reliability analysis. Since these approximated failure domains, which gradually approach the critical failure domain, allow for sufficiently sampling deep into the critical one, the Kriging model of the high-fidelity performance function can be accurately constructed with the sufficient number of candidate samples. As a result, the conditional probability of the critical failure domain, and thus the rare event probability, are finally estimated with high precision. Three illustrative examples, including a concrete arch dam subject to both hydrostatic and sediment accumulation loads, are investigated to validate the proposed method

Nash equilibrium seeking in non-cooperative heterogeneous multi-robot systems via output regulation

This paper presents a study on Nash equilibrium seeking in noncooperative games within multi-robot systems, a topic of increasing importance in diverse sectors including civil, security, and military. Unlike conventional approaches where players can directly observe the actions of others, our method assumes limited visibility, where players can only communicate through an undirected and connected communication graph. We introduce a novel distributed control approach, integrating gradient play with a consensus protocol. This method facilitates effective Nash equilibrium seeking by leveraging information shared among neighboring robots in heterogeneous linear dynamic systems. The proposed solution employs a high-level distributed Nash equilibrium-seeking algorithm, serving as an optimal reference generator for each robot to track the Nash equilibrium, and an advanced output regulation technique, aiming to regulate the output (e.g., position) of the robots with respect to the obtained references. Theoretical analysis confirms the convergence of our algorithm through Lyapunov stability analysis. The effectiveness and practical applicability of our approach are validated through numerical simulations and empirical testing with physical robots, highlighting its efficacy and utility in real-world scenarios

A consensus statement on child and family health during the COVID-19 pandemic and recommendations for post-pandemic recovery and re-build.

IntroductionAs health systems struggled to respond to the catastrophic effects of SARS-CoV-2, infection prevention and control measures significantly impacted on the delivery of non-COVID children's and family health services. The prioritisation of public health measures significantly impacted supportive relationships, revealed their importance for both mental and physical health and well-being. Drawing on findings from an expansive national collaboration, and with the well-being of children and young people in mind, we make recommendations here for post-pandemic recovery and re-build.MethodsThis consensus statement is derived from a cross-disciplinary collaboration of experts. Working together discursively, we have synthesised evidence from collaborative research in child and family health during the COVID-19 pandemic. We have identified and agreed priorities areas for both action and learning, which we present as recommendations for research, healthcare practice, and policy.ResultsThe synthesis led to immediate recommendations grouped around what to retain and what to remove from "pandemic" provision and what to reinstate from pre-pandemic, healthcare provision in these services. Longer-term recommendations for action were also made. Those relevant to children's well-being concern equity and relational healthcare.DiscussionThe documented evidence-base of the effects of the pandemic on children's and family services is growing, providing foundations for the post-pandemic recovery and re-setting of child and family health services and care provision. Recommendations contribute to services better aligning with the values of equity and relational healthcare, whilst providing wider consideration of care and support for children and families in usual vs. extra-ordinary health system shock circumstances

Antipsychotic management in general practice: serial cross-sectional study (2011-2020).

BackgroundLong-term use of antipsychotics confers increased risk of cardiometabolic disease. Ongoing need should be reviewed regularly by psychiatrists.AimTo explore trends in antipsychotic management in general practice, and the proportions of patients prescribed antipsychotics receiving psychiatrist review.Design and settingSerial cross-sectional study using linked general practice and hospital data in Wales (2011-2020).MethodParticipants were adults (aged ≥18 years) registered with general practices in Wales. Outcome measures were prevalence of patients receiving ≥6 antipsychotic prescriptions annually, the proportion of patients prescribed antipsychotics receiving annual psychiatrist review, and the proportion of patients prescribed antipsychotics who were registered on the UK serious mental illness, depression, and/or dementia registers, or not on any of these registers.ResultsPrevalence of adults prescribed long-term antipsychotics increased from 1.055% (95% confidence interval [CI] = 1.041 to 1.069) in 2011 to 1.448% (95% CI = 1.432 to 1.464) in 2020. The proportion receiving annual psychiatrist review decreased from 59.6% (95% CI = 58.9 to 60.4) in 2011 to 52.0% (95% CI = 51.4 to 52.7) in 2020. The proportion of overall antipsychotic use prescribed to patients on the serious mental illness register decreased from 50.0% (95% CI = 49.4 to 50.7) in 2011 to 43.6% (95% CI = 43.0 to 44.1) by 2020.ConclusionPrevalence of long-term antipsychotic use is increasing. More patients are managed by GPs without psychiatrist review and are not on monitored disease registers; they thus may be less likely to undergo cardiometabolic monitoring and miss opportunities to optimise or deprescribe antipsychotics. These trends pose risks for patients and need to be addressed urgently

Probably proximal pebbles? An outcrop-constrained quantitative analysis of clast transport distances in the Lower Triassic Sherwood Sandstone Group, UK

Lower Triassic Sherwood Sandstone Group fluvial strata in England record northward flow of the Budleighensis river system through fault-bounded syn-rift grabens from the Wessex Basin to the Irish Sea Basin. Pebbles in the Sherwood Sandstones are typically argued to originate mostly from the Armorican Massif, Northern France. Northwards transport should mean pebbles decrease in concentration northwards, yet it is also argued that pebble-rich strata in several basins across central and northern England suggest multiple sediment source areas contributed pebbles along the river system. To further test and constrain this multiple source hypothesis pebble concentration data were used to constrain a simple source-to-sink exponential decay model predicting pebble concentration versus distance from source. The model was inverted on number and location of clast input sources to calculate best-fit matches. Results indicate three distinct pebble sources best fit these data, with major tributaries originating in the Welsh Massif and the London-Brabant Massif, supporting a multiple sediment source interpretation for the Early Triassic river system. Multiple sediment input points increase prediction uncertainty for Triassic strata. Unanticipated heterogeneity has implications for practical subsurface prediction, for example related to proposed carbon and nuclear waste storage

Public Goods Games in Disease Evolution and Spread

Abstract Cooperation arises in nature at every scale, from within cells to entire ecosystems. Public goods games (PGGs) are used to represent scenarios characterised by the conflict/dilemma between choosing cooperation as a socially optimal strategy and defection as an individually optimal strategy. Evolutionary game theory is often used to analyse the dynamics of behaviour emergence in this context. Here, we focus on PGGs arising in the disease modelling of cancer evolution and the spread of infectious diseases. We use these two systems as case studies for the development of the theory and applications of PGGs, which we succinctly review. We also posit that applications of evolutionary game theory to decision-making in cancer, such as interactions between a clinician and a tumour, can learn from the PGGs studied in epidemiology, where cooperative behaviours such as quarantine and vaccination compliance have been more thoroughly investigated. Furthermore, instances of cellular-level cooperation observed in cancers point to a corresponding area of potential interest for modellers of other diseases, be they viral, bacterial or otherwise. We aim to demonstrate the breadth of applicability of PGGs in disease modelling while providing a starting point for those interested in quantifying cooperation arising in healthcare.</jats:p