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Improved job quality will make longer working lives in the aged care sector more sustainable. We interviewed 20 older aged care workers to identify which job characteristics are significant for health and to identify policy remedies. Workers take pleasure and pride in responding autonomously to a care recipient's situation, developing understanding, maintaining morale and performing intimate bodily care with dignity. However, a shortage of staff time requires workers to take a task-oriented approach. This causes worker stress and diminishes their desire and capacity to delay retirement. In the Australian context, regulating minimum staffing is the most suitable policy response.

Context. The origin of the observed population of Wolf-Rayet (WR) stars in low-metallicity galaxies, such as the Small Magellanic Cloud (SMC), is not yet understood. Standard, single-star evolutionary models predict that WR stars should stem from very massive O-type star progenitors, but these are very rare. On the other hand, binary evolutionary models predict that WR stars could originate from primary stars in close binaries. Aims. We conduct an analysis of the massive O star, AzV 14, to spectroscopically determine its fundamental and stellar wind parameters, which are then used to investigate evolutionary paths from the O-type to the WR stage with stellar evolutionary models. Methods. Multi-epoch UV and optical spectra of AzV 14 are analyzed using the non-local thermodynamic equilibrium (LTE) stellar atmosphere code PoWR. An optical TESS light curve was extracted and analyzed using the PHOEBE code. The obtained parameters are put into an evolutionary context, using the MESA code. Results. AzV 14 is a close binary system with a period of P = 3.7058 ± 0.0013 d. The binary consists of two similar main sequence stars with masses of M1, 2 ≈ 32 M⊙. Both stars have weak stellar winds with mass-loss rates of log Ṁ/(M⊙ yr−1) = −7.7 ± 0.2. Binary evolutionary models can explain the empirically derived stellar and orbital parameters, including the position of the AzV 14 components on the Hertzsprung-Russell diagram, revealing its current age of 3.3 Myr. The model predicts that the primary will evolve into a WR star with Teff ≈ 100 kK, while the secondary, which will accrete significant amounts of mass during the first mass transfer phase, will become a cooler WR star with Teff ≈ 50 kK. Furthermore, WR stars that descend from binary components that have accreted significant amount of mass are predicted to have increased oxygen abundances compared to other WR stars. This model prediction is supported by a spectroscopic analysis of a WR star in the SMC. Conclusions. Inspired b

The BRIght Target Explorer (BRITE) mission collects photometric time series in two passbands aiming to investigate stellar structure and evolution. Since their launches in the years 2013 and 2014, the constellation of five BRITE nano-satellites has observed a total of more than 700 individual bright stars in 64 fields. Some targets have been observed multiple times. Thus, the total time base of the data sets acquired for those stars can be as long as nine years. Our aim is to provide a complete description of ready-to-use BRITE data, to show the scientific potential of the BRITE-Constellation data by identifying the most interesting targets, and to demonstrate and encourage how scientists can use these data in their research. We apply a decorrelation process to the automatically reduced BRITE-Constellation data to correct for instrumental effects. We perform a statistical analysis of the light curves obtained for the 300 stars observed in the first 14 fields during the first ~2.5 years of the mission. We also perform cross-identification with the International Variable Star Index. We present the data obtained by the BRITE-Constellation mission in the first 14 fields it observed from November 2013 to April 2016. We also describe the properties of the data for these fields and the 300 stars observed in them. Using these data, we detected variability in 64% of the presented sample of stars. Sixty-four stars or 21.3% of the sample have not yet been identified as variable in the literature and their data have not been analysed in detail. They can therefore provide valuable scientific material for further research. All data are made publicly available through the BRITE Public Data Archive and the Canadian Astronomy Data Centre., Comment: accepted by Astronomy & Astrophysics, 13 pages main text, 22 pages of appendix

Recent catastrophic fires in Australia and North America have raised broad‐scale questions about how the cessation of Indigenous burning practices has impacted fuel accumulation and structure. For sustainable coexistence with fire, a better understanding of the ancient nexus between humans and flammable landscapes is needed. We used novel palaeoecological modeling and charcoal compilations to reassess evidence for changes in land cover and fire activity, focusing on southeast Australia before and after British colonization. Here, we provide what we believe is the first quantitative evidence that the region’s forests and woodlands contained fewer shrubs and more grass before colonization. Changes in vegetation, fuel structures, and connectivity followed different trajectories in different vegetation types. The pattern is best explained by the disruption of Indigenous vegetation management caused by European settlement. Combined with climate‐change impacts on fire weather and drought, the widespread absence of Indigenous fire management practices likely preconditioned fire‐prone regions for wildfires of unprecedented extent.

Organised attacks on a computer system to test existing defences, i.e., penetration testing, have been used extensively to evaluate network security. However, penetration testing is a time-consuming process. Additionally, establishing a strategy that resembles a real cyber-attack typically requires in-depth knowledge of the cybersecurity domain. This paper presents a novel architecture, named deep cascaded reinforcement learning agents, or CRLA, that addresses large discrete action spaces in an autonomous penetration testing simulator, where the number of actions exponentially increases with the complexity of the designed cybersecurity network. Employing an algebraic action decomposition strategy, CRLA is shown to find the optimal attack policy in scenarios with large action spaces faster and more stably than a conventional deep Q-learning agent, which is commonly used as a method for applying artificial intelligence to autonomous penetration testing.