915 research outputs found

    In-n-out: The Gas Cycle From Dwarfs To Spiral Galaxies

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    We examine the scalings of galactic outflows with halo mass across a suite of 20 high-resolution cosmological zoom galaxy simulations covering halo masses in the range 10^9.5-10^12\M. These simulations self-consistently generate outflows from the available supernova energy in a manner that successfully reproduces key galaxy observables, including the stellar mass–halo mass, Tully–Fisher, and mass–metallicity relations. We quantify the importance of ejective feedback to setting the stellar mass relative to the efficiency of gas accretion and star formation. Ejective feedback is increasingly important as galaxy mass decreases; we find an effective mass loading factor that scales as v-circ-2.2, with an amplitude and shape that are invariant with redshift. These scalings are consistent with analytic models for energy-driven wind, based solely on the halo potential. Recycling is common: about half of the outflow mass across all galaxy masses is later reaccreted. The recycling timescale is typically ~1 Gyr, virtually independent of halo mass. Recycled material is reaccreted farther out in the disk and with typically ~2–3 times more angular momentum. These results elucidate and quantify how the baryon cycle plausibly regulates star formation and alters the angular momentum distribution of disk material across the halo mass range where most cosmic star formation occurs

    Living well with dementia: An exploratory matched analysis of minority ethnic and white people with dementia and carers participating in the IDEAL programme

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    \ua9 2024 The Authors. International Journal of Geriatric Psychiatry published by John Wiley & Sons Ltd.Objectives: The increasing heterogeneity of the population of older people is reflected in an increasing number of people with dementia and carers drawn from minority ethnic groups. Data from the IDEAL study are used to compare indices of ‘living well’ among people with dementia and carers from ethnic minority groups with matched white peers. Methods: We used an exploratory cross-sectional case-control design to compare ‘living well’ for people with dementia and carers from minority ethnic and white groups. Measures for both groups were quality of life, life satisfaction, wellbeing, loneliness, and social isolation and, for carers, stress, relationship quality, role captivity and caring competence. Results: The sample of people with dementia consisted of 20 minority ethnic and 60 white participants and for carers 15 and 45 respectively. People with dementia from minority ethnic groups had poorer quality of life (−4.74, 95% CI: −7.98 to −1.50) and higher loneliness (1.72, 95% CI: 0.78–2.66) whilst minority ethnic carers had higher stress (8.17, 95% CI: 1.72–14.63) and role captivity (2.00, 95% CI: 0.43–3.57) and lower relationship quality (−9.86, 95% CI: −14.24 to −5.48) than their white peers. Conclusion: Our exploratory study suggests that people with dementia from minority ethnic groups experience lower quality of life and carers experience higher stress and role captivity and lower relationship quality than their white peers. Confirmatory research with larger samples is required to facilitate analysis of the experiences of specific minority ethnic groups and examine the factors contributing to these disadvantages

    Multi-Decadal Changes in Mangrove Extent, Age and Species in the Red River Estuaries of Viet Nam

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    This research investigated the performance of four different machine learning supervised image classifiers: artificial neural network (ANN), decision tree (DT), random forest (RF), and support vector machine (SVM) using SPOT-7 and Sentinel-1 images to classify mangrove age and species in 2019 in a Red River estuary, typical of others found in northern Viet Nam. The four classifiers were chosen because they are considered to have high accuracy, however, their use in mangrove age and species classifications has thus far been limited. A time-series of Landsat images from 1975 to 2019 was used to map mangrove extent changes using the unsupervised classification method of iterative self-organizing data analysis technique (ISODATA) and a comparison with accuracy of K-means classification, which found that mangrove extent has increased, despite a fall in the 1980s, indicating the success of mangrove plantation and forest protection efforts by local people in the study area. To evaluate the supervised image classifiers, 183 in situ training plots were assessed, 70% of them were used to train the supervised algorithms, with 30% of them employed to validate the results. In order to improve mangrove species separations, Gram–Schmidt and principal component analysis image fusion techniques were applied to generate better quality images. All supervised and unsupervised (2019) results of mangrove age, species, and extent were mapped and accuracy was evaluated. Confusion matrices were calculated showing that the classified layers agreed with the ground-truth data where most producer and user accuracies were greater than 80%. The overall accuracy and Kappa coefficients (around 0.9) indicated that the image classifications were very good. The test showed that SVM was the most accurate, followed by DT, ANN, and RF in this case study. The changes in mangrove extent identified in this study and the methods tested for using remotely sensed data will be valuable to monitoring and evaluation assessments of mangrove plantation projects
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