The performance benefits of deploying tidal turbines in close side-by-side proximity to exploit constructive interference effects are demonstrated experimentally using two 1.2 m diameter turbines. The turbines are arrayed side-by-side at 1/4 diameter tip-to-tip spacing, and their performance compared with that of a single rotor. Tests were completed in the 25 m diameter, 2 m deep wave and current FloWave Ocean Energy Research facility. A detailed assessment of inflow conditions at different control points is used to understand the impact that rotors, designed for high blockage conditions, have on the approach flow. After accounting for global blockage, a 10.8 % uplift in the twin-turbine-averaged power coefficient, relative to that for a single turbine, is found for the turbine design speed, at the expense of a 5.2 % increase in thrust coefficient and 3.1 % increase in tip-speed-ratio. Flowfield mapping demonstrated flow effects at array and device scale including array bypass flows and jetting between turbines. Azimuthal variation of blade root flapwise and edgewise bending moments show that the turbines interact in a beneficial manner, with additional and sustained loading peaks as the blades pass in close proximity to the neighbouring rotor. Peak performance for the twin turbines occurred at a higher tip-speed-ratio than for the single turbine, which is consistent with the twin turbines exerting a higher thrust on the flow to achieve maximum power. The twin turbine performance variation with tip-speed-ratio is found to be more gradual than for the single turbine. Using differential rotor speed control we observe that array performance is robust to small differences in neighbouring rotor operating point. Through these experiments we demonstrate that there is a substantial, achievable performance benefit from closely arraying turbines for side-by-side operation and designing them for constructive interference.

Welfare states allocate and redistribute resources across different groups. For the social legitimacy of welfare states, public support of redistributive processes and outcomes is crucial. An important aspect in this context is the deservingness or non-deservingness of benefit recipients from the perspective of those who both financially contribute to the system and potentially benefit from it. We invited a random sample of the German labour force to participate in an online-survey. Using a factorial survey experiment, we described fictitious unemployed persons with different attributes and asked survey participants on the just maximum benefit duration for each particular case. Judgements regarding just benefit durations vary along the criteria of reciprocity, control, attitude and need: Respondents grant longer unemployment benefits to older jobseekers, as well as to jobseekers who became involuntarily unemployed, had stable employment careers, have to care for the elderly or are sole earners in the household.

We report on the generation and delivery of 10.2 PW peak power laser pulses, using the High Power Laser System at the Extreme Laser Infrastructure – Nuclear Physics facility. In this work we demonstrate for the first time, to the best of our knowledge, the compression and propagation of full energy, full aperture, laser pulses that reach a power level of more than 10 PW.

Social media platforms allow users to share news, ideas, thoughts, and opinions on a global scale. Data processing methods allow researchers to automate the collection and interpretation of social media posts for efficient and valuable disease surveillance. Data derived from social media and internet search trends have been used successfully for monitoring and forecasting disease outbreaks such as Zika, Dengue, MERS, and Ebola viruses. More recently, data derived from social media have been used to monitor and model disease incidence during the coronavirus disease 2019 (COVID-19) pandemic. We discuss the use of social media for disease surveillance.

Masturbation is a common sexual practice in men, and saliva is often used as a lubricant during masturbation by men who have sex with men. However, the role of saliva use during masturbation in the transmission of chlamydia is still unclear. We developed population-level, susceptible-infected-susceptible compartmental models to explore the role of saliva use during masturbation on the transmission of chlamydia at multiple anatomical sites. In this study, we simulated both solo masturbation and mutual masturbation. Our baseline model did not include masturbation but included transmission routes (anal sex, oral-penile sex, rimming, kissing and sequential sexual practices) we have previously validated (model 1). We added masturbation to model 1 to develop the second model (model 2). We calibrated the model to five clinical datasets separately to assess the effects of masturbation on the prevalence of site-specific infection. The inclusion of masturbation (model 2) significantly worsened the ability of the models to replicate the prevalence of C. trachomatis. Using model 2 and the five data sets, we estimated that saliva use during masturbation was responsible for between 3.9% [95% confidence interval (CI) 2.0–6.8] and 6.2% (95% CI 3.8–10.5) of incident chlamydia cases at all sites. Our models suggest that saliva use during masturbation is unlikely to play a major role in chlamydia transmission between men, and even if it does have a role, about one in seven cases of urethral chlamydia might arise from masturbation.

Group Name: VHA Center for Antimicrobial Stewardship and Prevention of Antimicrobial Resistance (CASPAR) Background: Antimicrobial stewardship programs (ASPs) are advised to measure antimicrobial consumption as a metric for audit and feedback. However, most ASPs lack the tools necessary for appropriate risk adjustment and standardized data collection, which are critical for peer-program benchmarking. We created a system that automatically extracts antimicrobial use data and patient-level factors for risk-adjustment and a dashboard to present risk-adjusted benchmarking metrics for ASP within the Veterans’ Health Administration (VHA). Methods: We built a system to extract patient-level data for antimicrobial use, procedures, demographics, and comorbidities for acute inpatient and long-term care units at all VHA hospitals utilizing the VHA’s Corporate Data Warehouse (CDW). We built baseline negative binomial regression models to perform risk-adjustments based on patient- and unit-level factors using records dated between October 2016 and September 2018. These models were then leveraged both retrospectively and prospectively to calculate observed-to-expected ratios of antimicrobial use for each hospital and for specific units within each hospital. Data transformation and applications of risk-adjustment models were automatically performed within the CDW database server, followed by monthly scheduled data transfer from the CDW to the Microsoft Power BI server for interactive data visualization. Frontline antimicrobial stewards at 10 VHA hospitals participated in the project as pilot users. Results: Separate baseline risk-adjustment models to predict days of therapy (DOT) for all antibacterial agents were created for acute-care and long-term care units based on 15,941,972 patient days and 3,011,788 DOT between October 2016 and September 2018 at 134 VHA hospitals. Risk adjustment models include month, unit types (eg, intensive care unit [ICU] vs non-ICU for acute care), specialty, age, gender, comorbidities (50 and 30 factors for acute care and long-term care, respectively), and preceding procedures (45 and 24 procedures for acute care and long-term care, respectively). We created additional models for each antimicrobial category based on National Healthcare Safety Network definitions. For each hospital, risk-adjusted benchmarking metrics and a monthly ranking within the VHA system were visualized and presented to end users through the dashboard (an example screenshot in Figure 1). Conclusions: Developing an automated surveillance system for antimicrobial consumption and risk-adjustment benchmarking using an electronic medical record data warehouse is feasible and can potentially provide valuable tools for ASPs, especially at hospitals with no or limited local informatics expertise. Future efforts will evaluate the effectiveness of dashboards in these settings.

Funding: No

Disclosures: None

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Engineered products have economic, environmental, and social impacts, which comprise the major dimensions of sustainability. This paper seeks to determine the interaction between design parameters when the social impacts are incorporated into the design process. Social impact evaluation is increasing in importance similar to what has happened with environmental impact consideration in recent years in the design of engineered products. Concurrently, research into new airship design has increased, however airships have yet to be reintroduced at a large scale and for a range of applications in society. Although airships have the potential for positive environmental and economic impacts, the social impacts are still rarely considered. This paper presents a case study of the hypothetical introduction of airships in the Amazon to help local farmers transport their produce to market. It explores the design space in terms of the airship's social impacts connected to the design parameters. The social impacts are found to be dependent not only on the social factors and airship design parameters, but also on the farmer-airship system, suggesting that socio-technical systems design will benefit from integrated social impact metric analysis.

We present an overview of the SkyMapper optical follow-up programme for gravitational-wave event triggers from the LIGO/Virgo observatories, which aims at identifying early GW170817-like kilonovae out to $\sim200\,\mathrm{Mpc}$ distance. We describe our robotic facility for rapid transient follow-up, which can target most of the sky at $\delta<+10\deg $ to a depth of $i_\mathrm{AB}\approx 20\,\mathrm{mag}$ . We have implemented a new software pipeline to receive LIGO/Virgo alerts, schedule observations and examine the incoming real-time data stream for transient candidates. We adopt a real-bogus classifier using ensemble-based machine learning techniques, attaining high completeness ( $\sim98\%$ ) and purity ( $\sim91\%$ ) over our whole magnitude range. Applying further filtering to remove common image artefacts and known sources of transients, such as asteroids and variable stars, reduces the number of candidates by a factor of more than 10. We demonstrate the system performance with data obtained for GW190425, a binary neutron star merger detected during the LIGO/Virgo O3 observing campaign. In time for the LIGO/Virgo O4 run, we will have deeper reference images allowing transient detection to $i_\mathrm{AB}\approx 21\,\mathrm{mag}$ .

Although rare, children with active coronavirus disease 2019 are at risk of developing malignant arrhythmia. Herein, we present the first paediatric case of refractory ventricular tachycardia from acute fulminant myocarditis secondary to acute COVID-19 infection. This 5-year-old boy required venoarterial extracorporeal membrane oxygenation support, but made a complete recovery without significant morbidity.