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Individuals who identify as sexual and gender minorities, including lesbian, gay, bisexual, transgender, queer, and others (LGBTQ+) are more likely to experience poorer health than their heterosexual and cisgender counterparts. One primary source that drives these health disparities is minority stress (i.e., chronic and social stressors unique to LGBTQ+ communities' experiences adapting to the dominant culture). This stress is frequently expressed in LGBTQ+ users' posts on social media platforms. However, these expressions are not just straightforward manifestations of minority stress. They involve linguistic complexity (e.g., idiom or lexical diversity), rendering them challenging for many traditional natural language processing methods to detect. In this work, we designed a hybrid model using Graph Neural Networks (GNN) and Bidirectional Encoder Representations from Transformers (BERT), a pre-trained deep language model to improve the classification performance of minority stress detection. We experimented with our model on a benchmark social media dataset for minority stress detection (LGBTQ+ MiSSoM+). The dataset is comprised of 5,789 human-annotated Reddit posts from LGBTQ+ subreddits. Our approach enables the extraction of hidden linguistic nuances through pretraining on a vast amount of raw data, while also engaging in transductive learning to jointly develop representations for both labeled training data and unlabeled test data. The RoBERTa-GCN model achieved an accuracy of 0.86 and an F1 score of 0.86, surpassing the performance of other baseline models in predicting LGBTQ+ minority stress. Improved prediction of minority stress expressions on social media could lead to digital health interventions to improve the wellbeing of LGBTQ+ people-a community with high rates of stress-sensitive health problems., Comment: This paper is accepted in 2024 IEEE 11th International Conference on Data Science and Advanced Analytics (DSAA)

We use an existing laboratory facility for space hardware calibration in vacuum to study the impact of energetic ions on water ice. The experiment is intended to simulate the conditions on the surface of Jupiter's icy moons. We present first results of ion sputtering in a sample of porous ice, including the first experimental results for sulphur ion sputtering of ice. The results confirm theoretical predictions and extrapolations from previous sputtering experiments obtained at different impact angles for non-porous water ice., Comment: Upon feedback from an anonymous referee we decided to first run additional tests on charging properties of ice surfaces and potential countermeasures before submitting a new version of this article for publication

Since 2008 December, the Interstellar Boundary Explorer (IBEX) has been making detailed observations of neutrals from the boundaries of the heliosphere using two neutral atom cameras with overlapping energy ranges. The unexpected, yet defining feature discovered by IBEX is a Ribbon that extends over the energy range from about 0.2 to 6 keV. This Ribbon is superposed on a more uniform, globally distributed heliospheric neutral population. With some important exceptions, the focus of early IBEX studies has been on neutral atoms with energies greater than approx. 0.5 keV. With nearly three years of science observations, enough low-energy neutral atom measurements have been accumulated to extend IBEX observations to energies less than approx. 0.5 keV. Using the energy overlap of the sensors to identify and remove backgrounds, energy spectra over the entire IBEX energy range are produced. However, contributions by interstellar neutrals to the energy spectrum below 0.2 keV may not be completely removed. Compared with spectra at higher energies, neutral atom spectra at lower energies do not vary much from location to location in the sky, including in the direction of the IBEX Ribbon. Neutral fluxes are used to show that low energy ions contribute approximately the same thermal pressure as higher energy ions in the heliosheath. However, contributions to the dynamic pressure are very high unless there is, for example, turbulence in the heliosheath with fluctuations of the order of 50-100 km/s.