The paper describes a multilevel, multichannel R-matrix code, AZURE, for applications in nuclear astrophysics. The code allows simultaneous analysis and extrapolation of low-energy particle scattering, capture, and reaction cross sections of relevance to stellar hydrogen, helium, and carbon burning. The paper presents a summary of R-matrix theory, code description, and a number of applications to demonstrate the applicability and versatility of AZURE.

About a year after core collapse supernova, dust starts to condense in the ejecta. In meteorites, a fraction of C-rich presolar grains (e.g., silicon carbide (SiC) grains of Type-X and low density graphites) are identified as relics of these events, according to the anomalous isotopic abundances. Several features of these abundances remain unexplained and challenge the understanding of core-collapse supernovae explosions and nucleosynthesis. We show, for the first time, that most of the measured C-rich grain abundances can be accounted for in the C-rich material from explosive He burning in core-collapse supernovae with high shock velocities and consequent high temperatures. The inefficiency of the 12 C(α,γ) 16 O reaction relative to the rest of the α-capture chain at T > 3.5×10 8 K causes the deepest He-shell material to be carbon rich and silicon rich, and depleted in oxygen. The isotopic ratio predictions in part of this material, defined here as the C/Si zone, are in agreement with the grain data. The high-temperature explosive conditions that our models reach at the bottom of the He shell, can also be representative of the nucleosynthesis in hypernovae or in the high-temperature tail of a distribution of conditions in asymmetric supernovae. Finally, our predictions are consistent with the observation of large 44 Ca/ 40 Ca observed in the grains. This is due to the production of 44 Ti together with 40 Ca in the C/Si zone, and/or to the strong depletion of 40 Ca by neutron captures.

The overall aim of this article is to contribute to the further development of the area of risk analysis and risk management in the International Organization for for Standardization (ISO) standards by strengthening its scientific basis. Industrial standards, especially ISO standards, are the tools organizations use to manage their risk, through following their guidance and complying with their requirements. Organizations confirm their compliance with these standards through certification, which means that they heavily depend upon the quality of the ISO standards to enable them to effectively manage their risk. The purpose of this study is to investigate what guidance is given on key elements of risk management and how well ISO standards are aligned with state-of-the-art risk management literature. Eighteen ISO standards, all addressing risk management, were reviewed in this study with regard to risk terminology and guidance. The results of the study confirm the increasing importance of risk management for business. However, the study also shows a lack of guidance on doing risk analysis in the industrial standards examined. The ISO management system standards and guidelines are not aligned with the scientific literature on risk and are not appropriate for the management of risk arising from complex interactions and emergent behavior that is inherent in present-day sociotechnical systems.

Whereas in most studies conducted previously the effect of automation bias has been investigated in terms of an instantaneous decision, this study is aimed at quantifying its duration. Automation bias is modeled as a stochastic process using a unimodal log-log probability distribution. To validate the model, an experiment using an Airbus A320 fixed base flight simulator with a malfunction on the auto throttle was executed with 35 licensed pilots. The effect of pilot experience is investigated; results show that less experienced pilots are on average less sensitive to automation bias but have more variation in performance than more experienced pilots.