The Rising Stellar Velocity Dispersion of M87 from Integrated Starlight

We have measured the line-of-sight velocity distribution from integrated stellar light at two points in the outer halo of M87 (NGC 4486), the second-rank galaxy in the Virgo Cluster. The data were taken at R = 480" ($\sim 41.5$ kpc) and R = 526" ($\sim 45.5$ kpc) along the SE major axis. The second moment for a non-parametric estimate of the full velocity distribution is $420 \pm 23$ km/s and $577 \pm 35$ km/s respectively. There is intriguing evidence in the velocity profiles for two kinematically distinct stellar components at the position of our pointing. Under this assumption we employ a two-Gaussian decomposition and find the primary Gaussian having rest velocities equal to M87 (consistent with zero rotation) and second moments of $383 \pm 32$ km/s and $446 \pm 43$ km/s respectively. The asymmetry seen in the velocity profiles suggests that the stellar halo of M87 is not in a relaxed state and confuses a clean dynamical interpretation. That said, either measurement (full or two component model) shows a rising velocity dispersion at large radii, consistent with previous integrated light measurements, yet significantly higher than globular cluster measurements at comparable radial positions. These integrated light measurements at large radii, and the stark contrast they make to the measurements of other kinematic tracers, highlight the rich kinematic complexity of environments like the center of the Virgo Cluster and the need for caution when interpreting kinematic measurements from various dynamical tracers.Comment: 16 pages, 5 figures; accepted for publication in The Astrophysical Journa

A Method Worth Telling:Using Story Completion to Understand Social Work Responses to Discriminatory Abuse

Story completion methods have not yet been used in social work research, but the method has significant potential in this area. This paper reports on findings of a qualitative story completion study, which set out to understand professional responses to discriminatory abuse in English safeguarding adults practice. Fifty-six social worker and social care worker participants responded to a ‘story stem’, which refers to the opening lines of a story, continuing a story they choose to tell in response. In this instance, the story stem introduces a fictional scenario involving a social worker who is visiting an adult who has experienced discriminatory abuse. Story completion was chosen because it does not require self-report and this was useful given the under-reporting of discriminatory abuse. Story completion is appropriate for studying taboo or sensitive topics because it is less exposing, producing stories rather than accounts of one’s practice. Story completion also allowed contrast and comparison across different characteristics that might be targeted in discriminatory abuse, spotlighting divergent responses to discrimination based on transgender identity, race and mental ill-health. Dramaturgical narrative analysis was used to make sense of the resulting stories and three narratives were identified: anxious allies, affirmative advocates and administrative assessors. There were a small number of outliers who did not complete stories based on the guidelines provided. The results suggest workforce development needs in relation to discriminatory abuse. The article concludes with a reflection on the ways in which social work research can draw on story completion methods in the future

Reinforcement Learning for Battery Management in Dairy Farming

Dairy farming is a particularly energy-intensive part of the agriculture sector. Effective battery management is essential for renewable integration within the agriculture sector. However, controlling battery charging/discharging is a difficult task due to electricity demand variability, stochasticity of renewable generation, and energy price fluctuations. Despite the potential benefits of applying Artificial Intelligence (AI) to renewable energy in the context of dairy farming, there has been limited research in this area. This research is a priority for Ireland as it strives to meet its governmental goals in energy and sustainability. This research paper utilizes Q-learning to learn an effective policy for charging and discharging a battery within a dairy farm setting. The results demonstrate that the developed policy significantly reduces electricity costs compared to the established baseline algorithm. These findings highlight the effectiveness of reinforcement learning for battery management within the dairy farming sector.Comment: This paper has been accepted at the 2023 Artificial Intelligence for Sustainability (AI4S) Workshop, at 26th European Conference on Artificial Intelligence ECAI 202

A Multi-Agent Systems Approach for Peer-to-Peer Energy Trading in Dairy Farming

To achieve desired carbon emission reductions, integrating renewable generation and accelerating the adoption of peer-to-peer energy trading is crucial. This is especially important for energy-intensive farming, like dairy farming. However, integrating renewables and peer-to-peer trading presents challenges. To address this, we propose the Multi-Agent Peer-to-Peer Dairy Farm Energy Simulator (MAPDES), enabling dairy farms to participate in peer-to-peer markets. Our strategy reduces electricity costs and peak demand by approximately 30% and 24% respectively, while increasing energy sales by 37% compared to the baseline scenario without P2P trading. This demonstrates the effectiveness of our approach.Comment: Proc. of the Artificial Intelligence for Sustainability, ECAI 2023, Eunika et al. (eds.), Sep 30- Oct 1, 2023, https://sites.google.com/view/ai4s. 202