r-Java 2.0: the nuclear physics

[Aims:] We present r-Java 2.0, a nucleosynthesis code for open use that performs r-process calculations as well as a suite of other analysis tools. [Methods:] Equipped with a straightforward graphical user interface, r-Java 2.0 is capable of; simulating nuclear statistical equilibrium (NSE), calculating r-process abundances for a wide range of input parameters and astrophysical environments, computing the mass fragmentation from neutron-induced fission as well as the study of individual nucleosynthesis processes. [Results:] In this paper we discuss enhancements made to this version of r-Java, paramount of which is the ability to solve the full reaction network. The sophisticated fission methodology incorporated into r-Java 2.0 which includes three fission channels (beta-delayed, neutron-induced and spontaneous fission) as well as computation of the mass fragmentation is compared to the upper limit on mass fission approximation. The effects of including beta-delayed neutron emission on r-process yield is studied. The role of coulomb interactions in NSE abundances is shown to be significant, supporting previous findings. A comparative analysis was undertaken during the development of r-Java 2.0 whereby we reproduced the results found in literature from three other r-process codes. This code is capable of simulating the physical environment of; the high-entropy wind around a proto-neutron star, the ejecta from a neutron star merger or the relativistic ejecta from a quark nova. As well the users of r-Java 2.0 are given the freedom to define a custom environment. This software provides an even platform for comparison of different proposed r-process sites and is available for download from the website of the Quark-Nova Project: http://quarknova.ucalgary.ca/Comment: 26 pages, 18 figures, 1 tabl

Birth outcomes by type of attendance at antenatal education: An observational study

Background: Antenatal education aims to prepare expectant parents for pregnancy, birth, and parenthood. Studies have reported antenatal education teaching breathing and relaxation methods for pain relief, termed psychoprophylaxis, is associated with reduction in caesarean section rates compared with general birth and parenting classes. Given the rising rates of caesarean section, we aimed to determine whether there was a difference in mode of birth in women based on the type of antenatal education attended. Materials and methods: A cross-sectional antenatal survey of nulliparous women ≥28 weeks gestation with a singleton pregnancy was conducted in two maternity hospitals in Sydney, Australia in 2018. Women were asked what type of antenatal education they attended and sent a follow-up survey post-birth. Hospital birth data were also obtained. Education was classified into four groups: psychoprophylaxis, birth and parenting, other, or none. Results: Five hundred and five women with birth data were included. A higher proportion of women who attended psychoprophylaxis education had a vaginal birth (instrumental/spontaneous) (79%) compared with women who attended birth and parenting, other or no education (69%, 67%, 60%, respectively P = 0.045). After adjusting for maternal characteristics, birth and hospital factors, the association was attenuated (odds ratio 2.03; 95% CI 0.93–4.43). Conclusions: Women who attended psychoprophylaxis couple-based education had a trend toward higher rates of vaginal birth. Randomised trials comparing different types of antenatal education are required to determine whether psychoprophylaxis education can reduce caesarean section rates and improve other birth outcomes

Exploring sociodemographic correlates of suicide stigma in Australia: Baseline cross-sectional survey findings from the life-span suicide prevention trial studies

The risk of suicidal behaviour in Australia varies by age, sex, sexual preference and Indigenous status. Suicide stigma is known to affect suicide rates and help-seeking for suicidal crises. The aim of this study was to investigate the sociodemographic correlates of suicide stigma to assist in prevention efforts. We surveyed community members and individuals who had attended specific emergency departments for suicidal crisis. The respondents were part of a large-scale suicide prevention trial in New South Wales, Australia. The data collected included demographic characteristics, measures of help-seeking and suicide stigma. The linear regression analyses conducted sought to identify the factors associated with suicide stigma. The 5426 participants were predominantly female (71.4 %) with a mean (SD) age of 41.7 (14.8) years, and 3.9 % were Indigenous. Around one-third of participants reported a previous suicide attempt (n = 1690, 31.5 %) with two-thirds (n = 3545, 65.3 %) seeking help for suicidal crisis in the past year. Higher stigma scores were associated with Indigenous status (β 0.123, 95 % CI 0.074 – 0.172), male sex (β 0.527, 95 % CI 0.375 – 0.626) and regional residence (β 0.079, 95 % CI 0.015 – 0.143). Lower stigma scores were associated with younger age (β − 0.002, 95 % CI − 0.004 – − 0.001), mental illness (β − 0.095, 95 % CI − 0.139 to − 0.050), male bisexuality (β − 0.202, 95 % CI − 0.351 to − 0.052) and males who glorified suicide (β − 0.075, 95 % CI − 0.119 to − 0.031). These results suggested that suicide stigma differed across the community, varying significantly by sex, sexual orientation and Indigenous status. Targeted educational programs to address suicide stigma could assist in suicide prevention efforts

Octupole transitions in the 208Pb region

The 208Pb region is characterised by the existence of collective octupole states. Here we populated such states in 208Pb + 208Pb deep-inelastic reactions. γ-ray angular distribution measurements were used to infer the octupole character of several E3 transitions. The octupole character of the 2318 keV 17− → 14+ in 208Pb, 2485 keV 19/2 − → 13/2 + in 207Pb, 2419 keV 15/2 − → 9/2 + in 209Pb and 2465 keV 17/2 + → 11/2 − in 207Tl transitions was demonstrated for the first time. In addition, shell model calculations were performed using two different sets of two-body matrix elements. Their predictions were compared with emphasis on collective octupole states.This work is supported by the Science and Technology Facilities Council (STFC), UK, US Department of Energy, Office of Nuclear Physics, under Contract No. DEAC02-06CH11357 and DE-FG02-94ER40834, NSF grant PHY-1404442

Spectral quantification of nonlinear behaviour of the nearshore seabed and correlations with potential forcings at Duck, N.C., U.S.A

Local bathymetric quasi-periodic patterns of oscillation are identified from monthly profile surveys taken at two shore-perpendicular transects at the USACE field research facility in Duck, North Carolina, USA, spanning 24.5 years and covering the swash and surf zones. The chosen transects are the two furthest (north and south) from the pier located at the study site. Research at Duck has traditionally focused on one or more of these transects as the effects of the pier are least at these locations. The patterns are identified using singular spectrum analysis (SSA). Possible correlations with potential forcing mechanisms are discussed by 1) doing an SSA with same parameter settings to independently identify the quasi-periodic cycles embedded within three potentially linked sequences: monthly wave heights (MWH), monthly mean water levels (MWL) and the large scale atmospheric index known as the North Atlantic Oscillation (NAO) and 2) comparing the patterns within MWH, MWL and NAO to the local bathymetric patterns. The results agree well with previous patterns identified using wavelets and confirm the highly nonstationary behaviour of beach levels at Duck; the discussion of potential correlations with hydrodynamic and atmospheric phenomena is a new contribution. The study is then extended to all measured bathymetric profiles, covering an area of 1100m (alongshore) by 440m (cross-shore), to 1) analyse linear correlations between the bathymetry and the potential forcings using multivariate empirical orthogonal functions (MEOF) and linear correlation analysis and 2) identify which collective quasi-periodic bathymetric patterns are correlated with those within MWH, MWL or NAO, based on a (nonlinear) multichannel singular spectrum analysis (MSSA). (...continued in submitted paper)Comment: 50 pages, 3 tables, 8 figure

The variational Bayesian approach to fitting mixture models to circular wave direction data

The emerging variational Bayesian (VB) technique for approximate Bayesian statistical inference is a nonsimulation- based and time-efficient approach. It provides a useful, practical alternative to other Bayesian statistical approaches such as Markov chain Monte Carlo–based techniques, particularly for applications involving large datasets. This article reviews the increasingly popular VB statistical approach and illustrates how it can be used to fit Gaussian mixture models to circular wave direction data. This is done by taking the straightforward approach of padding the data; this method involves adding a repeat of a complete cycle of the data to the existing dataset to obtain a dataset on the real line. The padded dataset can then be analyzed using the standard VB technique. This results in a practical, efficient approach that is also appropriate for modeling other types of circular, or directional, data such as wind direction

Stillbirth risk prediction using machine learning for a large cohort of births from Western Australia, 1980–2015

Quantification of stillbirth risk has potential to support clinical decision-making. Studies that have attempted to quantify stillbirth risk have been hampered by small event rates, a limited range of predictors that typically exclude obstetric history, lack of validation, and restriction to a single classifier (logistic regression). Consequently, predictive performance remains low, and risk quantification has not been adopted into antenatal practice. The study population consisted of all births to women in Western Australia from 1980 to 2015, excluding terminations. After all exclusions there were 947,025 livebirths and 5,788 stillbirths. Predictive models for stillbirth were developed using multiple machine learning classifiers: regularised logistic regression, decision trees based on classification and regression trees, random forest, extreme gradient boosting (XGBoost), and a multilayer perceptron neural network. We applied 10-fold cross-validation using independent data not used to develop the models. Predictors included maternal socio-demographic characteristics, chronic medical conditions, obstetric complications and family history in both the current and previous pregnancy. In this cohort, 66% of stillbirths were observed for multiparous women. The best performing classifier (XGBoost) predicted 45% (95% CI: 43%, 46%) of stillbirths for all women and 45% (95% CI: 43%, 47%) of stillbirths after the inclusion of previous pregnancy history. Almost half of stillbirths could be potentially identified antenatally based on a combination of current pregnancy complications, congenital anomalies, maternal characteristics, and medical history. Greatest sensitivity is achieved with addition of current pregnancy complications. Ensemble classifiers offered marginal improvement for prediction compared to logistic regression

Stillbirth risk prediction using machine learning for a large cohort of births from Western Australia, 1980–2015

Quantification of stillbirth risk has potential to support clinical decision-making. Studies that have attempted to quantify stillbirth risk have been hampered by small event rates, a limited range of predictors that typically exclude obstetric history, lack of validation, and restriction to a single classifier (logistic regression). Consequently, predictive performance remains low, and risk quantification has not been adopted into antenatal practice. The study population consisted of all births to women in Western Australia from 1980 to 2015, excluding terminations. After all exclusions there were 947,025 livebirths and 5,788 stillbirths. Predictive models for stillbirth were developed using multiple machine learning classifiers: regularised logistic regression, decision trees based on classification and regression trees, random forest, extreme gradient boosting (XGBoost), and a multilayer perceptron neural network. We applied 10-fold cross-validation using independent data not used to develop the models. Predictors included maternal socio-demographic characteristics, chronic medical conditions, obstetric complications and family history in both the current and previous pregnancy. In this cohort, 66% of stillbirths were observed for multiparous women. The best performing classifier (XGBoost) predicted 45% (95% CI: 43%, 46%) of stillbirths for all women and 45% (95% CI: 43%, 47%) of stillbirths after the inclusion of previous pregnancy history. Almost half of stillbirths could be potentially identified antenatally based on a combination of current pregnancy complications, congenital anomalies, maternal characteristics, and medical history. Greatest sensitivity is achieved with addition of current pregnancy complications. Ensemble classifiers offered marginal improvement for prediction compared to logistic regression