The Field Guide: Applying <i>Making it Count</i> to health promotion activity with homosexually active men

This Field Guide considers a range of methods used to carry out health promotion with homosexually active men. It is a companion document to Making it count: a collaborative planning framework to reduce the incidence of HIV infection during sex between men (Hickson et al., 2003). Like Making it count, this document will be reviewed every two to three years and accompanied by training opportunities. The authors welcome comments and suggestions on this document and its use. These can be sent to: [email protected] or [email protected]. "Briefing papers" that add to the content of this guide will be produced as part of the CHAPS sector development programme. These will be available periodically from www. chapsonline.org.uk. Making it count describes a co-ordinated national framework to reduce HIV incidence occurring as a consequence of sex between men. It is intended for workers, managers, policy makers, legislators, health professionals or anyone with an investment in reducing HIV incidence among homosexually active men. This Field Guide is written for gay men's HIV health promoters. It places the theory, goals and strategic aims contained in Making it count in the context of day-to-day health promotion activity. It was developed through a range of formal interviews and informal discussion with more than 40 managers and key workers with experience and expertise in specific areas of HIV health promotion for homosexually active men. It concentrates mainly on direct contact work (Chapters 3 to 7), but also considers other types of health promotion that benefit homosexually active men by influencing the structures they live within (Chapter 8). Section one (Chapters 1 and 2) provides an overview of Making it count and the relationship between this document and that main framework. It outlines the key strategic aims of Making it count and contextualises what follows. Section two concerns direct contact with homosexually active men. Chapters 3, 4, 5 and 6 deal with different methods of carrying out direct contact work with this population. Chapter 7 considers the different target groups within the entire population of homosexually active men. It examines how to prioritise target groups using epidemiological and needs data and how best to target different groups in various settings. Section three (Chapter 8) addresses other types of health promotion interventions that are necessary including policy, community and service interventions. These are the interventions needed in order to facilitate direct contact interventions and attend to the broader determinants of sexual health for homosexually active men. It is anticipated that some (NHS) commissioners would benefit from reading this document in order to further their understanding of the range of work that they could fund. However, this document is not an implementation plan for the NHS in relation to HIV incidence among homosexually active men. Rather, Terrence Higgins Trust are currently in discussion with the Department of Health concerning further work to support the use of Making it count as the basis for Primary Care Trusts' planning and purchasing of HIV prevention activity for homosexually active men

Win Prediction in Esports: Mixed-Rank Match Prediction in Multi-player Online Battle Arena Games

Esports has emerged as a popular genre for players as well as spectators, supporting a global entertainment industry. Esports analytics has evolved to address the requirement for data-driven feedback, and is focused on cyber-athlete evaluation, strategy and prediction. Towards the latter, previous work has used match data from a variety of player ranks from hobbyist to professional players. However, professional players have been shown to behave differently than lower ranked players. Given the comparatively limited supply of professional data, a key question is thus whether mixed-rank match datasets can be used to create data-driven models which predict winners in professional matches and provide a simple in-game statistic for viewers and broadcasters. Here we show that, although there is a slightly reduced accuracy, mixed-rank datasets can be used to predict the outcome of professional matches, with suitably optimized configurations

Diffusion model approach to simulating electron-proton scattering events

Generative AI is a fast-growing area of research offering various avenues for exploration in high-energy nuclear physics. In this work, we explore the use of generative models for simulating electron-proton collisions relevant to experiments like CEBAF and the future Electron-Ion Collider (EIC). These experiments play a critical role in advancing our understanding of nucleons and nuclei in terms of quark and gluon degrees of freedom. The use of generative models for simulating collider events faces several challenges such as the sparsity of the data, the presence of global or event-wide constraints, and steeply falling particle distributions. In this work, we focus on the implementation of diffusion models for the simulation of electron-proton scattering events at EIC energies. Our results demonstrate that diffusion models can accurately reproduce relevant observables such as momentum distributions and correlations of particles, momentum sum rules, and the leading electron kinematics, all of which are of particular interest in electron-proton collisions. Although the sampling process is relatively slow compared to other machine learning architectures, we find diffusion models can generate high-quality samples. We foresee various applications of our work including inference for nuclear structure, interpretable generative machine learning, and searches of physics beyond the Standard Model.Comment: 14 pages, 10 figure

Ground-penetrating radar observations of enhanced biological activity in a sandbox reactor

In this study, we evaluate the use of ground-penetrating radar (GPR) to investigate the effects of bacterial activity in water saturated sand. A 90-day laboratory-scale controlled experiment was conducted in a flow-through polycarbonate sandbox using groundwater from the Kansas River alluvial aquifer as inoculum. After 40 days of collecting baseline data, bacterial growth was stimulated in the sandbox by the addition of a carbon and nutrient solution on a weekly basis. Radar signal travel time and attenuation were shown to increase downgradient of the nutrient release wells relative to upgradient locations. After 60 days, the frequency of nutrient injections was increased to twice per week, after which gaseous bubbles were visually observed downgradient of the nutrient release wells. Visual observation of active gas production correlated spatially and temporally with a rapid decrease in radar signal travel time, confirming that GPR can monitor the generation of biogenic gases in this system. Analysis of the sediments indicated microbial lipid biomass increased by approximately one order of magnitude and there were no changes in the inorganic carbon content of bulk sediment mineralogy. These findings suggest that the increase in biomass and gas production may be responsible for the observed changes in radar signal travel time reported in this study. Therefore, this study provides evidence that GPR can be used to monitor biological activity in water saturated sand.Funding for this project was through the National Science Foundation CAREER grant 0134545 awarded to J.F. Devlin and NSF EAR/IF-0345445 for acquisition of GPR instrumentation awarded to G. Tsoflias. The opinions, findings, and recommendations of this study are the views the author(s) and do not necessarily reflect the views and opinions of the National Science Foundation. We would like to thank Mike McGlashan, Kwan Yee Cheng, Kelly Peterson, Lindsay Mayer, and Breanna Huff for assistance with this project. We also thank two anonymous reviewers for their helpful comments that led to the improvement of this manuscript

An Exploration of HTML5, Flash, and Javascript - Building a Presentation Engine

Abstract HTML5 is an emerging standard that provides new features and capabilities that overlap with those traditionally provided by tools like Flash. Because the standard is still emerging there are no clear guidelines or trade-offs to help choose between using the different technologies. We demonstrate how HTML5 can be used to create a presentation engine, previously only possible in technologies like Flash. The presentations contain various rich and interactive media, including deep zoom viewing, videos, navigation control and sequencing of the presentation slides. These features demonstrate the capabilities of HTML5, combined with Javascript, and the techniques needed to use them

Using Association Rule Mining to Predict Opponent Deck Content in Android: Netrunner

As part of their design, card games often include information that is hidden from opponents and represents a strategic advantage if discovered. A player that can discover this information will be able to alter their strategy based on the nature of that information, and therefore become a more competent opponent. In this paper, we employ association rule-mining techniques for predicting item multisets, and show them to be effective in predicting the content of Netrunner decks. We then apply different modifications based on heuristic knowledge of the Netrunner game, and show the effectiveness of techniques which consider this knowledge during rule generation and prediction

Packing polymorphism of dapivirine and its impact on the performance of a dapivirine-releasing silicone elastomer vaginal ring

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