We present the first results from a new backend on the Australian Square Kilometre Array Pathfinder, the Commensal Realtime ASKAP Fast Transient COherent (CRACO) upgrade. CRACO records millisecond time resolution visibility data, and searches for dispersed fast transient signals including fast radio bursts (FRB), pulsars, and ultra-long period objects (ULPO). With the visibility data, CRACO can localise the transient events to arcsecond-level precision after the detection. Here, we describe the CRACO system and report the result from a sky survey carried out by CRACO at 110-ms resolution during its commissioning phase. During the survey, CRACO detected two FRBs (including one discovered solely with CRACO, FRB 20231027A), reported more precise localisations for four pulsars, discovered two new RRATs, and detected one known ULPO, GPM J1839 $-$10, through its sub-pulse structure. We present a sensitivity calibration of CRACO, finding that it achieves the expected sensitivity of 11.6 Jy ms to bursts of 110 ms duration or less. CRACO is currently running at a 13.8 ms time resolution and aims at a 1.7 ms time resolution before the end of 2024. The planned CRACO has an expected sensitivity of 1.5 Jy ms to bursts of 1.7 ms duration or less and can detect $10\times$ more FRBs than the current CRAFT incoherent sum system (i.e. 0.5 $-$2 localised FRBs per day), enabling us to better constrain the models for FRBs and use them as cosmological probes.

Generation of science-ready data from processed data products is one of the major challenges in next-generation radio continuum surveys with the Square Kilometre Array (SKA) and its precursors, due to the expected data volume and the need to achieve a high degree of automated processing. Source extraction, characterization, and classification are the major stages involved in this process. In this work we focus on the classification of compact radio sources in the Galactic plane using both radio and infrared images as inputs. To this aim, we produced a curated dataset of $\sim$20 000 images of compact sources of different astronomical classes, obtained from past radio and infrared surveys, and novel radio data from pilot surveys carried out with the Australian SKA Pathfinder. Radio spectral index information was also obtained for a subset of the data. We then trained two different classifiers on the produced dataset. The first model uses gradient-boosted decision trees and is trained on a set of pre-computed features derived from the data, which include radio-infrared colour indices and the radio spectral index. The second model is trained directly on multi-channel images, employing convolutional neural networks. Using a completely supervised procedure, we obtained a high classification accuracy (F1-score > 90%) for separating Galactic objects from the extragalactic background. Individual class discrimination performances, ranging from 60% to 75%, increased by 10% when adding far-infrared and spectral index information, with extragalactic objects, PNe and Hii regions identified with higher accuracies. The implemented tools and trained models were publicly released and made available to the radioastronomical community for future application on new radio data.

We demonstrate the importance of radio selection in probing heavily obscured galaxy populations. We combine Evolutionary Map of the Universe (EMU) Early Science data in the Galaxy and Mass Assembly (GAMA) G23 field with the GAMA data, providing optical photometry and spectral line measurements, together with Wide-field Infrared Survey Explorer (WISE) infrared (IR) photometry, providing IR luminosities and colours. We investigate the degree of obscuration in star-forming galaxies, based on the Balmer decrement (BD), and explore how this trend varies, over a redshift range of $0<z<0.345$. We demonstrate that the radio-detected population has on average higher levels of obscuration than the parent optical sample, arising through missing the lowest BD and lowest mass galaxies, which are also the lower star formation rate (SFR) and metallicity systems. We discuss possible explanations for this result, including speculation around whether it might arise from steeper stellar initial mass functions in low mass, low SFR galaxies.

We present a comparison between the performance of a selection of source finders (SFs) using a new software tool called Hydra. The companion paper, Paper I, introduced the Hydra tool and demonstrated its performance using simulated data. Here we apply Hydra to assess the performance of different source finders by analysing real observational data taken from the Evolutionary Map of the Universe (EMU) Pilot Survey. EMU is a wide-field radio continuum survey whose primary goal is to make a deep ($20\mu$Jy/beam RMS noise), intermediate angular resolution ($15^{\prime\prime}$), 1 GHz survey of the entire sky south of $+30^{\circ}$ declination, and expecting to detect and catalogue up to 40 million sources. With the main EMU survey it is highly desirable to understand the performance of radio image SF software and to identify an approach that optimises source detection capabilities. Hydra has been developed to refine this process, as well as to deliver a range of metrics and source finding data products from multiple SFs. We present the performance of the five SFs tested here in terms of their completeness and reliability statistics, their flux density and source size measurements, and an exploration of case studies to highlight finder-specific limitations.

The latest generation of radio surveys are now producing sky survey images containing many millions of radio sources. In this context it is highly desirable to understand the performance of radio image source finder (SF) software and to identify an approach that optimises source detection capabilities. We have created Hydra to be an extensible multi-SF and cataloguing tool that can be used to compare and evaluate different SFs. Hydra, which currently includes the SFs Aegean, Caesar, ProFound, PyBDSF, and Selavy, provides for the addition of new SFs through containerisation and configuration files. The SF input RMS noise and island parameters are optimised to a 90% ‘percentage real detections’ threshold (calculated from the difference between detections in the real and inverted images), to enable comparison between SFs. Hydra provides completeness and reliability diagnostics through observed-deep ($\mathcal{D}$) and generated-shallow ($\mathcal{S}$) images, as well as other statistics. In addition, it has a visual inspection tool for comparing residual images through various selection filters, such as S/N bins in completeness or reliability. The tool allows the user to easily compare and evaluate different SFs in order to choose their desired SF, or a combination thereof. This paper is part one of a two part series. In this paper we introduce the Hydra software suite and validate its $\mathcal{D/S}$ metrics using simulated data. The companion paper demonstrates the utility of Hydra by comparing the performance of SFs using both simulated and real images.

The building of online atomic and molecular databases for astrophysics and for other research fields started with the beginning of the internet. These databases have encompassed different forms: databases of individual research groups exposing their own data, databases providing collected data from the refereed literature, databases providing evaluated compilations, databases providing repositories for individuals to deposit their data, and so on. They were, and are, the replacement for literature compilations with the goal of providing more complete and in particular easily accessible data services to the users communities. Such initiatives involve not only scientific work on the data, but also the characterization of data, which comes with the “standardization” of metadata and of the relations between metadata, as recently developed in different communities. This contribution aims at providing a representative overview of the atomic and molecular databases ecosystem, which is available to the astrophysical community and addresses different issues linked to the use and management of data and databases. The information provided in this paper is related to the keynote lecture “Atomic and Molecular Databases: Open Science for better science and a sustainable world” whose slides can be found at DOI : doi.org/10.5281/zenodo.6979352 on the Zenodo repository connected to the “cb5-labastro” Zenodo Community (https://zenodo.org/communities/cb5-labastro).

There is evidence indicating that using the current UK energy feeding system to ration the present sheep flocks may underestimate their nutrient requirements. The objective of the present study was to address this issue by developing updated maintenance energy requirements for the current sheep flocks and evaluating if these requirements were influenced by a range of dietary and animal factors. Data (n = 131) used were collated from five experiments with sheep (5 to 18 months old and 29.0 to 69.8 kg BW) undertaken at the Agri-Food and Biosciences Institute of the UK from 2013 to 2017. The trials were designed to evaluate the effects of dietary type, genotype, physiological stage and sex on nutrient utilization and energetic efficiencies. Energy intake and output data were measured in individual calorimeter chambers. Energy balance (Eg) was calculated as the difference between gross energy intake and a sum of fecal energy, urine energy, methane energy and heat production. Data were analysed using the restricted maximum likelihood analysis to develop the linear relationship between Eg or heat production and metabolizable energy (ME) intake, with the effects of a range of dietary and animal factors removed. The net energy (NEm) and ME (MEm) requirements for maintenance derived from the linear relationship between Eg and ME intake were 0.358 and 0.486 MJ/kg BW0.75, respectively, which are 40% to 53% higher than those recommended in energy feeding systems currently used to ration sheep in the USA and the UK. Further analysis of the current dataset revealed that concentrate supplement, sire type or physiological stage had no significant effect on the derived NEm values. However, female lambs had a significantly higher NEm (0.352 v. 0.306 or 0.288 MJ/kg BW0.75) or MEm (0.507 v. 0.441 or 0.415 MJ/kg BW0.75) than those for male or castrated lambs. The present results indicate that using present energy feeding systems in the UK developed over 40 years ago to ration the current sheep flocks could underestimate maintenance energy requirements. There is an urgent need to update these systems to reflect the higher metabolic rates of the current sheep flocks.

Evidence from animal models indicates that exposure to an obesogenic or hyperglycemic intrauterine environment adversely impacts offspring kidney development and renal function. However, evidence from human studies has not been evaluated systematically. Therefore, the aim of this systematic review was to synthesize current research in humans that has examined the relationship between gestational obesity and/or diabetes and offspring kidney structure and function. Systematic electronic database searches were conducted of five relevant databases (CINAHL, Cochrane, EMBASE, MEDLINE and Scopus). Preferred Reporting Items for Systematic Reviews and Meta-analysis guidelines were followed, and articles screened by two independent reviewers generated nine eligible papers for inclusion. Six studies were assessed as being of ‘neutral’ quality, two of ‘negative’ and one ‘positive’ quality. Observational studies suggest that offspring exposed to a hyperglycemic intrauterine environment are more likely to display markers of renal dysfunction and are at higher risk of end-stage renal disease. There was limited and inconsistent evidence for a link between exposure to an obesogenic intrauterine environment and offspring renal outcomes. Offspring renal outcome measures across studies were diverse, with a large variation in offspring age at follow-up, limiting comparability across studies. The collective current body of evidence suggests that intrauterine exposure to maternal obesity and/or diabetes adversely impacts renal programming in offspring, with an increased risk of kidney disease in adulthood. Further high-quality, longitudinal, prospective cohort studies that measure indicators of offspring renal development and function, including fetal kidney volume and albuminuria, at standardized follow-up time points, are warranted.

In this paper we propose a new approach to study the Parisian ruin problem for spectrally negative Lévy processes. Since our approach is based on a hybrid observation scheme switching between discrete and continuous observations, we call it a temporal approach as opposed to the spatial approximation approach in the literature. Our approach leads to a unified proof for the underlying processes with bounded or unbounded variation paths, and our result generalizes Loeffen et al. (2013).

Weed resistance monitoring has been routinely conducted in the Northern Great Plains of Canada (Prairies) since the mid-1990s. Most recently, random surveys were conducted in Alberta in 2001, Manitoba in 2002, and Saskatchewan in 2003 totaling nearly 800 fields. In addition, nearly 1,300 weed seed samples were submitted by growers across the Prairies between 1996 and 2006 for resistance testing. Collected or submitted samples were screened for group 1 [acetyl-CoA carboxylase (ACCase) inhibitor] and/or group 2 [acetolactate synthase (ALS) inhibitor] resistance. Twenty percent of 565 sampled fields had an herbicide-resistant (HR) wild oat biotype. Most populations exhibited broad cross-resistance across various classes of group 1 or group 2 herbicides. In Manitoba, 22% of 59 fields had group 1–HR green foxtail. Group 2–HR biotypes of kochia were documented in Saskatchewan, common chickweed and spiny sowthistle in Alberta, and green foxtail and redroot pigweed in Manitoba. Across the Prairies, HR weeds are estimated to occur in fields covering an area of nearly 5 million ha. Of 1,067 wild oat seed samples submitted by growers and industry for testing between 1996 and 2006, 725 were group 1 HR, 34 group 2 HR, and 55 groups 1 and 2 HR. Of 80 submitted green foxtail samples, 26 were confirmed group 1 HR; most populations originated from southern Manitoba where the weed is most abundant. Similar to the field surveys, various group 2–HR biotypes were confirmed among submitted samples: kochia, wild mustard, field pennycress, Galium spp., common chickweed, and common hempnettle. Information from grower questionnaires indicates patterns of herbicide usage are related to location, changing with cropping system. Two herbicide modes of action most prone to select resistance, groups 1 and 2, continue to be widely and repeatedly used. There is little evidence that growers are aware of the level of resistance within their fields, but a majority have adopted herbicide rotations to proactively or reactively manage HR weeds.

Agricultural practices, other than herbicide use, can affect the rate of evolution of herbicide resistance in weeds. This study examined associations of farm management practices with the occurrence of herbicide (acetyl-CoA carboxylase or acetolactate synthase inhibitor)-resistant weeds, based upon a multi-year (2001 to 2003) random survey of 370 fields/growers from the Canadian Prairies. Herbicide-resistant weeds occurred in one-quarter of the surveyed fields. The primary herbicide-resistant weed species was wild oat, with lesser occurrence of green foxtail, kochia, common chickweed, spiny sowthistle, and redroot pigweed. The risk of weed resistance was greatest in fields with cereal-based rotations and least in fields with forage crops, fallow, or where three or more crop types were grown. Weed resistance risk also was greatest in conservation-tillage systems and particularly low soil disturbance no-tillage, possibly due to greater herbicide use or weed seed bank turnover. Large farms (> 400 ha) had a greater risk of weed resistance than smaller farms, although the reason for this association was unclear. The results of this study identify cropping system diversity as the foundation of proactive weed resistance management.