To explore the driving forces behind deformation twinning in Mg AZ31, a machine learning framework is utilized to mine data obtained from electron backscatter diffraction (EBSD) scans in order to extract correlations in physical characteristics that cause twinning. The results are intended to inform physics-based models of twin nucleation and growth. A decision tree learning environment is selected to capture the relationships between microstructure and twin formation; this type of model effectively highlights the more influential characteristics of the local microstructure. Trees are assembled to analyze both twin nucleation in a given grain, and twin propagation across grain boundaries. Each model reveals a unique combination of crystallographic attributes that affect twinning in the Mg. Twin nucleation is found to be mostly controlled by a combination of grain size, basal Schmid factor, and bulk dislocation density while twin propagation is affected most by grain boundary length, basal Schmid factor, angle from grain boundary plane to the RD plane, and grain boundary misorientation. The machine

Population genetics and understanding of mating systems provide fundamental information for conservation planning. Pairing these methods is a powerful tool in the study of threatened species, however, they are rarely applied in concert. We examined the mating system and used molecular genetics to measure pairwise kinship and the potential for inbreeding in Hibbertia spanantha, a critically endangered long‐lived shrub endemic to the Sydney Basin, Australia, as a model for conservation planning of species in highly fragmented populations. In situ hand pollination experiments demonstrated that the species is preferentially outcrossing, with limited ability to self‐pollinate (either autogamously or geitonogamously). Although population genetics confirmed high levels of kinship and clonality, there is currently enough population heterozygosity for successful open pollination, primarily through buzz pollination by Sweat Bees (Lasioglossum [Chilalictus]). High levels of clonality and population kinship in one population may be the cause of reduced fitness, identified because our outcrossing pollination treatment produced significantly more seeds with greater viability and seed mass than the open treatments. Differences in weight of filled (viable) seeds were identified between populations, although not treatments, where clonal dominance may be swamping pollinator foraging activities. Identification of species mating system, population reproductive capacity, and impacts of fragmentation on population genetic health provides a robust basis for strategic planning and conservation of this critically endangered species, including establishment of an ex situ population and genetic rescue through population augmentation. These methods are easily applicable and particularly relevant to other plant species with small populations or those occurring in fragmented systems.

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One role of artificial intelligence is to predict future events after learning from many previous observations. In materials science, various phenomena (such as crack nucleation) are difficult to predict because they have been insufficiently observed. Furthermore, observation is difficult, precisely because their location cannot be predicted, leading to a chicken and egg conundrum. This paper applies machine learning to the search for twin nucleation sites in a magnesium alloy, in an attempt to guide the observation of twin nucleation events in a microscope based on previous observations. As more data is obtained, the accuracy of the location prediction will increase. In the current case, the machine-learning tool achieved 85% accuracy for predicting the location of twin interactions with grain boundaries after several thousand observations. The resultant framework provides the first step towards an intelligent microscopy for efficient observation of stochastic events during in situ microscopy campaigns.

Characterization of distortion transfer and generation through fans with distorted inlet conditions enables progress towards designs with improved distortion tolerance. The abruptness of transition from undistorted to distorted total pressure regions at the inlet impacts the induced swirl profile and therefore the distortion transfer and generation. These impacts are characterized using URANS simulations of PBS Rotor 4 geometry under a variety of inlet distortion profiles. A 90° and a 135° sector, both of 15% total pressure distortion, are considered. Variants of each sector size, with decreasing levels of distortion transition abruptness, are each applied to the fan. Fourier-based distortion descriptors are used to quantify levels of distortion transfer and generation at axial locations through the fan, principally at the stator inlet. It is shown that a gradual transition in distortion at the inlet results in decreased levels of distortion transfer and generation. The flow physics resulting in this reduction are explored.