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 Bruss–Robertson–Steele (BRS) inequality bounds the expected number of items of random size which can be packed into a given suitcase. Remarkably, no independence assumptions are needed on the random sizes, which points to a simple explanation; the inequality is the integrated form of an $\omega$-by-$\omega$ inequality, as this note proves.

Substantial progress has been made in the standardization of nomenclature for paediatric and congenital cardiac care. In 1936, Maude Abbott published her Atlas of Congenital Cardiac Disease, which was the first formal attempt to classify congenital heart disease. The International Paediatric and Congenital Cardiac Code (IPCCC) is now utilized worldwide and has most recently become the paediatric and congenital cardiac component of the Eleventh Revision of the International Classification of Diseases (ICD-11). The most recent publication of the IPCCC was in 2017. This manuscript provides an updated 2021 version of the IPCCC.

The International Society for Nomenclature of Paediatric and Congenital Heart Disease (ISNPCHD), in collaboration with the World Health Organization (WHO), developed the paediatric and congenital cardiac nomenclature that is now within the eleventh version of the International Classification of Diseases (ICD-11). This unification of IPCCC and ICD-11 is the IPCCC ICD-11 Nomenclature and is the first time that the clinical nomenclature for paediatric and congenital cardiac care and the administrative nomenclature for paediatric and congenital cardiac care are harmonized. The resultant congenital cardiac component of ICD-11 was increased from 29 congenital cardiac codes in ICD-9 and 73 congenital cardiac codes in ICD-10 to 318 codes submitted by ISNPCHD through 2018 for incorporation into ICD-11. After these 318 terms were incorporated into ICD-11 in 2018, the WHO ICD-11 team added an additional 49 terms, some of which are acceptable legacy terms from ICD-10, while others provide greater granularity than the ISNPCHD thought was originally acceptable. Thus, the total number of paediatric and congenital cardiac terms in ICD-11 is 367. In this manuscript, we describe and review the terminology, hierarchy, and definitions of the IPCCC ICD-11 Nomenclature. This article, therefore, presents a global system of nomenclature for paediatric and congenital cardiac care that unifies clinical and administrative nomenclature.

The members of ISNPCHD realize that the nomenclature published in this manuscript will continue to evolve. The version of the IPCCC that was published in 2017 has evolved and changed, and it is now replaced by this 2021 version. In the future, ISNPCHD will again publish updated versions of IPCCC, as IPCCC continues to evolve.

We present a detailed analysis of the radio galaxy PKS $2250{-}351$ , a giant of 1.2 Mpc projected size, its host galaxy, and its environment. We use radio data from the Murchison Widefield Array, the upgraded Giant Metre-wavelength Radio Telescope, the Australian Square Kilometre Array Pathfinder, and the Australia Telescope Compact Array to model the jet power and age. Optical and IR data come from the Galaxy And Mass Assembly (GAMA) survey and provide information on the host galaxy and environment. GAMA spectroscopy confirms that PKS $2250{-}351$ lies at $z=0.2115$ in the irregular, and likely unrelaxed, cluster Abell 3936. We find its host is a massive, ‘red and dead’ elliptical galaxy with negligible star formation but with a highly obscured active galactic nucleus dominating the mid-IR emission. Assuming it lies on the local M– $\sigma$ relation, it has an Eddington accretion rate of $\lambda_{\rm EDD}\sim 0.014$ . We find that the lobe-derived jet power (a time-averaged measure) is an order of magnitude greater than the hotspot-derived jet power (an instantaneous measure). We propose that over the lifetime of the observed radio emission ( ${\sim} 300\,$ Myr), the accretion has switched from an inefficient advection-dominated mode to a thin disc efficient mode, consistent with the decrease in jet power. We also suggest that the asymmetric radio morphology is due to its environment, with the host of PKS $2250{-}351$ lying to the west of the densest concentration of galaxies in Abell 3936.

Information on the factors that cause or amplify foodborne illness outbreaks (contributing factors), such as ill workers or cross-contamination of food by workers, is critical to outbreak prevention. However, only about half of foodborne illness outbreaks reported to the United States’ Centers for Disease Control and Prevention (CDC) have an identified contributing factor, and data on outbreak characteristics that promote contributing factor identification are limited. To address these gaps, we analyzed data from 297 single-setting outbreaks reported to CDC's new outbreak surveillance system, which collects data from the environmental health component of outbreak investigations (often called environmental assessments), to identify outbreak characteristics associated with contributing factor identification. These analyses showed that outbreak contributing factors were more often identified when an outbreak etiologic agent had been identified, when the outbreak establishment prepared all meals on location and served more than 150 meals a day, when investigators contacted the establishment to schedule the environmental assessment within a day of the establishment being linked with an outbreak, and when multiple establishment visits were made to complete the environmental assessment. These findings suggest that contributing factor identification is influenced by multiple outbreak characteristics, and that timely and comprehensive environmental assessments are important to contributing factor identification. They also highlight the need for strong environmental health and food safety programs that have the capacity to complete such environmental assessments during outbreak investigations.

With an airborne lidar, we have observed massive plumes of condensate particles rising from wintertime leads in the Arctic Ocean. Some of these plumes reached an altitude of 4 km; some extended over 200 km down-wind from their surface source. Here we invert the lidar equation and use lidar backscatter data to infer particle concentrations within two such plumes. Assuming that the plumes consist of supercooled water droplets of radius 5 μm, we estimate typical concentrations of 3–6 × 105 droplets m-3 just above the leads. Concentrations within the plumes can still be as high as 104 droplets m-3 at an altitude of 3 km and 200 km down-wind from some leads. Had we assumed that the plume particles are ice spheres of radius 40 μm, concentrations would be just 100 times less than these.

We compare first-order (refractive) ionospheric effects seen by the MWA with the ionosphere as inferred from GPS data. The first-order ionosphere manifests itself as a bulk position shift of the observed sources across an MWA field of view. These effects can be computed from global ionosphere maps provided by GPS analysis centres, namely the CODE. However, for precision radio astronomy applications, data from local GPS networks needs to be incorporated into ionospheric modelling. For GPS observations, the ionospheric parameters are biased by GPS receiver instrument delays, among other effects, also known as receiver DCBs. The receiver DCBs need to be estimated for any non-CODE GPS station used for ionosphere modelling. In this work, single GPS station-based ionospheric modelling is performed at a time resolution of 10 min. Also the receiver DCBs are estimated for selected Geoscience Australia GPS receivers, located at Murchison Radio Observatory, Yarragadee, Mount Magnet and Wiluna. The ionospheric gradients estimated from GPS are compared with that inferred from MWA. The ionospheric gradients at all the GPS stations show a correlation with the gradients observed with the MWA. The ionosphere estimates obtained using GPS measurements show promise in terms of providing calibration information for the MWA.

GLEAM, the GaLactic and Extragalactic All-sky MWA survey, is a survey of the entire radio sky south of declination + 25° at frequencies between 72 and 231 MHz, made with the MWA using a drift scan method that makes efficient use of the MWA’s very large field-of-view. We present the observation details, imaging strategies, and theoretical sensitivity for GLEAM. The survey ran for two years, the first year using 40-kHz frequency resolution and 0.5-s time resolution; the second year using 10-kHz frequency resolution and 2 s time resolution. The resulting image resolution and sensitivity depends on observing frequency, sky pointing, and image weighting scheme. At 154 MHz, the image resolution is approximately 2.5 × 2.2/cos (δ + 26.7°) arcmin with sensitivity to structures up to ~ 10° in angular size. We provide tables to calculate the expected thermal noise for GLEAM mosaics depending on pointing and frequency and discuss limitations to achieving theoretical noise in Stokes I images. We discuss challenges, and their solutions, that arise for GLEAM including ionospheric effects on source positions and linearly polarised emission, and the instrumental polarisation effects inherent to the MWA’s primary beam.

The Murchison Widefield Array is a Square Kilometre Array Precursor. The telescope is located at the Murchison Radio–astronomy Observatory in Western Australia. The MWA consists of 4 096 dipoles arranged into 128 dual polarisation aperture arrays forming a connected element interferometer that cross-correlates signals from all 256 inputs. A hybrid approach to the correlation task is employed, with some processing stages being performed by bespoke hardware, based on Field Programmable Gate Arrays, and others by Graphics Processing Units housed in general purpose rack mounted servers. The correlation capability required is approximately 8 tera floating point operations per second. The MWA has commenced operations and the correlator is generating 8.3 TB day−1 of correlation products, that are subsequently transferred 700 km from the MRO to Perth (WA) in real-time for storage and offline processing. In this paper, we outline the correlator design, signal path, and processing elements and present the data format for the internal and external interfaces.

The science cases for incorporating high time resolution capabilities into modern radio telescopes are as numerous as they are compelling. Science targets range from exotic sources such as pulsars, to our Sun, to recently detected possible extragalactic bursts of radio emission, the so-called fast radio bursts (FRBs). Originally conceived purely as an imaging telescope, the initial design of the Murchison Widefield Array (MWA) did not include the ability to access high time and frequency resolution voltage data. However, the flexibility of the MWA’s software correlator allowed an off-the-shelf solution for adding this capability. This paper describes the system that records the 100 μs and 10 kHz resolution voltage data from the MWA. Example science applications, where this capability is critical, are presented, as well as accompanying commissioning results from this mode to demonstrate verification.

In this paper we solve the hedge fund manager's optimization problem in a model that allows for investors to enter and leave the fund over time depending on its performance. The manager's payoff at the end of the year will then depend not just on the terminal value of the fund level, but also on the lowest and the highest value reached over that time. We establish equivalence to an optimal stopping problem for Brownian motion; by approximating this problem with the corresponding optimal stopping problem for a random walk we are led to a simple and efficient numerical scheme to find the solution, which we then illustrate with some examples.