The newly introduced discipline of Population-Based Structural Health Monitoring (PBSHM) has been developed in order to circumvent the issue of data scarcity in “classical” SHM. PBSHM does this by using data across an entire population, in order to improve diagnostics for a single data-poor structure. The improvement of inferences across populations uses the machine-learning technology of transfer learning. In order that transfer makes matters better, rather than worse, PBSHM assesses the similarity of structures and only transfers if a threshold of similarity is reached. The similarity measures are implemented by embedding structures as models —Irreducible-Element (IE) models— in a graph space. The problem with this approach is that the construction of IE models is subjective and can suffer from author-bias, which may induce dissimilarity where there is none. This paper proposes that IE-models be transformed to a canonical form through reduction rules, in which possible sources of ambiguity have been removed. Furthermore, in order that other variations —outside the control of the modeller— are correctly dealt with, the paper introduces the idea of a reality model, which encodes details of the environment and operation of the structure. Finally, the effects of the canonical form on similarity assessments are investigated via a numerical population study. A final novelty of the paper is in the implementation of a neural-network-based similarity measure, which learns reduction rules from data; the results with the new graph-matching network (GMN) are compared with a previous approach based on the Jaccard index, from pure graph theory.

Regression is a fundamental prediction task common in data-centric engineering applications that involves learning mappings between continuous variables. In many engineering applications (e.g., structural health monitoring), feature-label pairs used to learn such mappings are of limited availability, which hinders the effectiveness of traditional supervised machine learning approaches. This paper proposes a methodology for overcoming the issue of data scarcity by combining active learning (AL) for regression with hierarchical Bayesian modeling. AL is an approach for preferentially acquiring feature-label pairs in a resource-efficient manner. In particular, the current work adopts a risk-informed approach that leverages contextual information associated with regression-based engineering decision-making tasks (e.g., inspection and maintenance). Hierarchical Bayesian modeling allow multiple related regression tasks to be learned over a population, capturing local and global effects. The information sharing facilitated by this modeling approach means that information acquired for one engineering system can improve predictive performance across the population. The proposed methodology is demonstrated using an experimental case study. Specifically, multiple regressions are performed over a population of machining tools, where the quantity of interest is the surface roughness of the workpieces. An inspection and maintenance decision process is defined using these regression tasks, which is in turn used to construct the active-learning algorithm. The novel methodology proposed is benchmarked against an uninformed approach to label acquisition and independent modeling of the regression tasks. It is shown that the proposed approach has superior performance in terms of expected cost—maintaining predictive performance while reducing the number of inspections required.

Increasing food intake or eating unhealthily after exercise may undermine attempts to manage weight, thereby contributing to poor population-level health. This scoping review aimed to synthesise the evidence on the psychology of changes to eating after exercise and explore why changes to eating after exercise occur. A scoping review of peer-reviewed literature was conducted in accordance with the Joanna Briggs Institute guidance. Search terms relating to exercise, eating behaviour, and compensatory eating were used. All study designs were included. Research in children, athletes, or animals was excluded. No country or date restrictions were applied. Twenty-three studies were identified. Ten experimental studies (nine acute, one chronic) manipulated the psychological experience of exercise, one intervention study directly targeted compensatory eating, seven studies used observational methods (e.g. diet diaries, 24-h recall) to directly measure compensatory eating after exercise, and five questionnaire studies measured beliefs about eating after exercise. Outcomes varied and included energy intake (kcal/kJ), portion size, food intake, food choice, food preference, dietary lapse, and self-reported compensatory eating. We found that increased consumption of energy-dense foods occurred after exercise when exercise was perceived as less enjoyable, less autonomous, or hard work. Personal beliefs, exercise motivation, and exercise enjoyment were key psychological determinants of changes to eating after exercise. Individuals may consume additional food to refuel their energy stores after exercise (psychological compensatory eating), or consume unhealthy or energy dense foods to reward themselves after exercise, especially if exercise is experienced negatively (post-exercise licensing), however the population-level prevalence of these behaviours is unknown.

Coastal wetlands are hotspots of carbon sequestration, and their conservation and restoration can help to mitigate climate change. However, there remains uncertainty on when and where coastal wetland restoration can most effectively act as natural climate solutions (NCS). Here, we synthesize current understanding to illustrate the requirements for coastal wetland restoration to benefit climate, and discuss potential paths forward that address key uncertainties impeding implementation. To be effective as NCS, coastal wetland restoration projects will accrue climate cooling benefits that would not occur without management action (additionality), will be implementable (feasibility) and will persist over management-relevant timeframes (permanence). Several issues add uncertainty to understanding if these minimum requirements are met. First, coastal wetlands serve as both a landscape source and sink of carbon for other habitats, increasing uncertainty in additionality. Second, coastal wetlands can potentially migrate outside of project footprints as they respond to sea-level rise, increasing uncertainty in permanence. To address these first two issues, a system-wide approach may be necessary, rather than basing cooling benefits only on changes that occur within project boundaries. Third, the need for NCS to function over management-relevant decadal timescales means methane responses may be necessary to include in coastal wetland restoration planning and monitoring. Finally, there is uncertainty on how much data are required to justify restoration action. We summarize the minimum data required to make a binary decision on whether there is a net cooling benefit from a management action, noting that these data are more readily available than the data required to quantify the magnitude of cooling benefits for carbon crediting purposes. By reducing uncertainty, coastal wetland restoration can be implemented at the scale required to significantly contribute to addressing the current climate crisis.

Despite the growing availability of sensing and data in general, we remain unable to fully characterize many in-service engineering systems and structures from a purely data-driven approach. The vast data and resources available to capture human activity are unmatched in our engineered world, and, even in cases where data could be referred to as “big,” they will rarely hold information across operational windows or life spans. This paper pursues the combination of machine learning technology and physics-based reasoning to enhance our ability to make predictive models with limited data. By explicitly linking the physics-based view of stochastic processes with a data-based regression approach, a derivation path for a spectrum of possible Gaussian process models is introduced and used to highlight how and where different levels of expert knowledge of a system is likely best exploited. Each of the models highlighted in the spectrum have been explored in different ways across communities; novel examples in a structural assessment context here demonstrate how these approaches can significantly reduce reliance on expensive data collection. The increased interpretability of the models shown is another important consideration and benefit in this context.

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.

We present the third data release from the Parkes Pulsar Timing Array (PPTA) project. The release contains observations of 32 pulsars obtained using the 64-m Parkes ‘Murriyang’ radio telescope. The data span is up to 18 yr with a typical cadence of 3 weeks. This data release is formed by combining an updated version of our second data release with $\sim$3 yr of more recent data primarily obtained using an ultra-wide-bandwidth receiver system that operates between 704 and 4032 MHz. We provide calibrated pulse profiles, flux density dynamic spectra, pulse times of arrival, and initial pulsar timing models. We describe methods for processing such wide-bandwidth observations and compare this data release with our previous release.

We studied how patient beliefs regarding the need for antibiotics, as measured by expectation scores, and antibiotic prescribing outcome affect patient satisfaction using data from 2,710 urgent-care visits. Satisfaction was affected by antibiotic prescribing among patients with medium–high expectation scores but not among patients with low expectation scores.

The mechanism through which developmental programming of offspring overweight/obesity following in utero exposure to maternal overweight/obesity operates is unknown but may operate through biologic pathways involving offspring anthropometry at birth. Thus, we sought to examine to what extent the association between in utero exposure to maternal overweight/obesity and childhood overweight/obesity is mediated by birth anthropometry. Analyses were conducted on a retrospective cohort with data obtained from one hospital system. A natural effects model framework was used to estimate the natural direct effect and natural indirect effect of birth anthropometry (weight, length, head circumference, ponderal index, and small-for-gestational age [SGA] or large-for-gestational age [LGA]) for the association between pre-pregnancy maternal body mass index (BMI) category (overweight/obese vs normal weight) and offspring overweight/obesity in childhood. Models were adjusted for maternal and child socio-demographics. Three thousand nine hundred and fifty mother–child dyads were included in analyses (1467 [57.8%] of mothers and 913 [34.4%] of children were overweight/obese). Results suggest that a small percentage of the effect of maternal pre-pregnancy BMI overweight/obesity on offspring overweight/obesity operated through offspring anthropometry at birth (weight: 15.5%, length: 5.2%, head circumference: 8.5%, ponderal index: 2.2%, SGA: 2.9%, and LGA: 4.2%). There was a small increase in the percentage mediated when gestational diabetes or hypertensive disorders were added to the models. Our study suggests that some measures of birth anthropometry mediate the association between maternal pre-pregnancy overweight/obesity and offspring overweight/obesity in childhood and that the size of this mediated effect is small.

Several reports have shown that doctoral and postdoctoral trainees in biomedical research pursue diverse careers that advance science meaningful to society. Several groups have proposed 3-tier career taxonomy to showcase these outcomes. This 3-tier taxonomy will be a valuable resource for institutions committed to greater transparency in reporting outcomes, to not only be transparent in reporting their own institutional data but also to lend greater power to a central repository.

The Numeniini is a tribe of 13 wader species (Scolopacidae, Charadriiformes) of which seven are Near Threatened or globally threatened, including two Critically Endangered. To help inform conservation management and policy responses, we present the results of an expert assessment of the threats that members of this taxonomic group face across migratory flyways. Most threats are increasing in intensity, particularly in non-breeding areas, where habitat loss resulting from residential and commercial development, aquaculture, mining, transport, disturbance, problematic invasive species, pollution and climate change were regarded as having the greatest detrimental impact. Fewer threats (mining, disturbance, problematic native species and climate change) were identified as widely affecting breeding areas. Numeniini populations face the greatest number of non-breeding threats in the East Asian-Australasian Flyway, especially those associated with coastal reclamation; related threats were also identified across the Central and Atlantic Americas, and East Atlantic flyways. Threats on the breeding grounds were greatest in Central and Atlantic Americas, East Atlantic and West Asian flyways. Three priority actions were associated with monitoring and research: to monitor breeding population trends (which for species breeding in remote areas may best be achieved through surveys at key non-breeding sites), to deploy tracking technologies to identify migratory connectivity, and to monitor land-cover change across breeding and non-breeding areas. Two priority actions were focused on conservation and policy responses: to identify and effectively protect key non-breeding sites across all flyways (particularly in the East Asian- Australasian Flyway), and to implement successful conservation interventions at a sufficient scale across human-dominated landscapes for species’ recovery to be achieved. If implemented urgently, these measures in combination have the potential to alter the current population declines of many Numeniini species and provide a template for the conservation of other groups of threatened species.

Although extinction risk has been found to have a consistent negative relationship with geographic range across wide temporal and taxonomic scales, the effect has been difficult to disentangle from factors such as sampling, ecological niche, or clade. In addition, studies of extinction risk have focused on benthic invertebrates with less work on planktic taxa. We employed a global set of 1114 planktic graptolite species from the Ordovician to lower Devonian to analyze the predictive power of species’ traits and abiotic factors on extinction risk, combining general linear models (GLMs), partial least-squares regression (PLSR), and permutation tests. Factors included measures of geographic range, sampling, and graptolite-specific factors such as clade, biofacies affiliation, shallow water tolerance, and age cohorts split at the base of the Katian and Rhuddanian stages.

The percent variance in durations explained varied substantially between taxon subsets from 12% to 45%. Overall commonness, the correlated effects of geographic range and sampling, was the strongest, most consistent factor (12–30% variance explained), with clade and age cohort adding up to 18% and other factors <10%. Surprisingly, geographic range alone contributed little explanatory power (<5%). It is likely that this is a consequence of a nonlinear relationship between geographic range and extinction risk, wherein the largest reductions in extinction risk are gained from moderate expansion of small geographic ranges. Thus, even large differences in range size between graptolite species did not lead to a proportionate difference in extinction risk because of the large average ranges of these species. Finally, we emphasize that the common practice of determining the geographic range of taxa from the union of all occurrences over their duration poses a substantial risk of overestimating the geographic scope of the realized ecological niche and, thus, of further conflating sampling effects on observed duration with the biological effects of range size on extinction risk.