Reliable population estimates are one of the most elementary needs for the management of wildlife, particularly for introduced ungulates on oceanic islands. We aimed to produce accurate and precise density estimates of Philippine deer (Rusa marianna) and wild pigs (Sus scrofa) on Guam using motion-triggered cameras combined with distance sampling to estimate densities from observations of unmarked animals while accounting for imperfect detection. We used an automated digital data processing pipeline for species recognition and to estimate the distance to detected species. Our density estimates were slightly lower than published estimates, consistent with management to reduce populations. We estimated the number of camera traps needed to obtain a 0.1 coefficient of variation was substantial, requiring > ten-fold increase in camera traps, while estimates with precision of 0.2 or 0.3 were more achievable, requiring doubling to quadrupling the number of camera traps. We provide best practices for establishing and conducting distance sampling with camera trap surveys for density estimation based on lessons learned during this study. Future studies should consider distance sampling with camera traps to efficiently survey and monitor unmarked animals, particularly medium-sized ungulates, in tropical, oceanic island ecosystems.

The Accelerating COVID-19 Therapeutic Interventions and Vaccines (ACTIV) Cross-Trial Statistics Group gathered lessons learned from statisticians responsible for the design and analysis of the 11 ACTIV therapeutic master protocols to inform contemporary trial design as well as preparation for a future pandemic. The ACTIV master protocols were designed to rapidly assess what treatments might save lives, keep people out of the hospital, and help them feel better faster. Study teams initially worked without knowledge of the natural history of disease and thus without key information for design decisions. Moreover, the science of platform trial design was in its infancy. Here, we discuss the statistical design choices made and the adaptations forced by the changing pandemic context. Lessons around critical aspects of trial design are summarized, and recommendations are made for the organization of master protocols in the future.

The U.S. Department of Agriculture–Agricultural Research Service (USDA-ARS) has been a leader in weed science research covering topics ranging from the development and use of integrated weed management (IWM) tactics to basic mechanistic studies, including biotic resistance of desirable plant communities and herbicide resistance. ARS weed scientists have worked in agricultural and natural ecosystems, including agronomic and horticultural crops, pastures, forests, wild lands, aquatic habitats, wetlands, and riparian areas. Through strong partnerships with academia, state agencies, private industry, and numerous federal programs, ARS weed scientists have made contributions to discoveries in the newest fields of robotics and genetics, as well as the traditional and fundamental subjects of weed–crop competition and physiology and integration of weed control tactics and practices. Weed science at ARS is often overshadowed by other research topics; thus, few are aware of the long history of ARS weed science and its important contributions. This review is the result of a symposium held at the Weed Science Society of America’s 62nd Annual Meeting in 2022 that included 10 separate presentations in a virtual Weed Science Webinar Series. The overarching themes of management tactics (IWM, biological control, and automation), basic mechanisms (competition, invasive plant genetics, and herbicide resistance), and ecosystem impacts (invasive plant spread, climate change, conservation, and restoration) represent core ARS weed science research that is dynamic and efficacious and has been a significant component of the agency’s national and international efforts. This review highlights current studies and future directions that exemplify the science and collaborative relationships both within and outside ARS. Given the constraints of weeds and invasive plants on all aspects of food, feed, and fiber systems, there is an acknowledged need to face new challenges, including agriculture and natural resources sustainability, economic resilience and reliability, and societal health and well-being.

The next generation of high-power lasers enables repetition of experiments at orders of magnitude higher frequency than what was possible using the prior generation. Facilities requiring human intervention between laser repetitions need to adapt in order to keep pace with the new laser technology. A distributed networked control system can enable laboratory-wide automation and feedback control loops. These higher-repetition-rate experiments will create enormous quantities of data. A consistent approach to managing data can increase data accessibility, reduce repetitive data-software development and mitigate poorly organized metadata. An opportunity arises to share knowledge of improvements to control and data infrastructure currently being undertaken. We compare platforms and approaches to state-of-the-art control systems and data management at high-power laser facilities, and we illustrate these topics with case studies from our community.

Every year, there are over 200 traumatic deaths at work in Australia. A government safety inspector usually investigates each incident. The investigation may lead to prosecution of the employer or another party deemed to have breached relevant legislation. However, little systematic research has examined the needs and interests of grieving families in this process. Drawing on interviews with 48 representatives of institutions that deal with deaths at work (including regulators, unions, employers, police and coronial officers), this article examines how they view the problems and experiences of families. Notwithstanding some recent improvements, findings indicate ongoing shortcomings in meeting the needs of families regarding information provision, involvement and securing justice.

We formulate and solve a generalized inverse Navier–Stokes problem for the joint velocity field reconstruction and boundary segmentation of noisy flow velocity images. To regularize the problem, we use a Bayesian framework with Gaussian random fields. This allows us to estimate the uncertainties of the unknowns by approximating their posterior covariance with a quasi-Newton method. We first test the method for synthetic noisy images of two-dimensional (2-D) flows and observe that the method successfully reconstructs and segments the noisy synthetic images with a signal-to-noise ratio (SNR) of three. Then we conduct a magnetic resonance velocimetry (MRV) experiment to acquire images of an axisymmetric flow for low (${\simeq }6$) and high (${>}30$) SNRs. We show that the method is capable of reconstructing and segmenting the low SNR images, producing noiseless velocity fields and a smooth segmentation, with negligible errors compared with the high SNR images. This amounts to a reduction of the total scanning time by a factor of 27. At the same time, the method provides additional knowledge about the physics of the flow (e.g. pressure) and addresses the shortcomings of MRV (i.e. low spatial resolution and partial volume effects) that otherwise hinder the accurate estimation of wall shear stresses. Although the implementation of the method is restricted to 2-D steady planar and axisymmetric flows, the formulation applies immediately to three-dimensional (3-D) steady flows and naturally extends to 3-D periodic and unsteady flows.

The UK's relationship with the European Union (EU) is now embodied in two principal legal instruments: the EU–UK Trade and Cooperation Agreement, which formally entered into force on 1 May 2021; and the Withdrawal Agreement, with its Protocol on Ireland/Northern Ireland, which continues to apply. Using a ‘building blocks’ framework for analysis of national health systems derived from the World Health Organisation, this article examines the likely impacts in the UK of this legal settlement on the National Health Service (NHS), health and social care. Specifically, we determine the extent to which the trade, cooperation and regulatory aspects of those legal measures support positive impacts for the NHS and social care. We show that, as there is clear support for positive health and care outcomes in only one of the 17 NHS ‘building blocks’, unless mitigating action is taken, the likely outcomes will be detrimental. However, as the legal settlement gives the UK a great deal of regulatory freedom, especially in Great Britain, we argue that it is crucial to track the effects of proposed new health and social care-related policy choices in the months and years ahead.