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.

Dientamoeba fragilis (D. fragilis) is an intestinal protozoan parasite with uncertain pathogenic potential. In the United States, data on D. fragilis in the era of molecular detection are limited. The aim of this retrospective chart review was to evaluate the epidemiology and clinical characteristics of D. fragilis cases identified using polymerase chain reaction assays between 2016 and 2024 at our academic medical centre located in Utah. We identified 28 unique cases with varying gastrointestinal symptomatology including diarrhoea, abdominal pain, nausea, vomiting, and bloating. Approximately half (52%) of patients with follow-up data demonstrated improvement in symptoms following initial treatment for D. fragilis. The overall prevalence of D. fragilis was low among those tested (0.6% positivity). Additional research, including case-control studies, is needed to better describe the etiologic role of D. fragilis.

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.

Contrasting light environments in rainforests generates changes in the characteristics of the leaves and in the herbivore community. In the present study, we carried out a reciprocal transplant experiment under natural conditions to determine the plasticity of leaf characteristics of plant species that grow in contrasting light environments in a Neotropical forest. We further explored the relationship between these traits and insect herbivory. We found that six woody species differ markedly in the phenotypic plasticity of leaf features. The specific leaf area, chlorophyll content, carbon content, nitrogen content, and leaf thickness of the most light-demanding species were highest in gaps, but their carbon/nitrogen ratios were higher under closed canopies. The herbivores were more abundant in gaps (5.9%–14.8%) than under closed canopy habitats (3.4%–6.1%) and seemingly associated to the plasticity of the leaf traits. We observed 47% more herbivores in gaps than under closed canopies. Our results suggest that the phenotypic plasticity of leaf traits depends on the identity of the plant species and its wood density, while herbivory seems to be affected by plant defence, low nutritional quality, or herbivore tolerance.

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.

Governments in sub-Saharan African countries aim to increase married women’s household decision-making autonomy as it remains a critical determinant of desirable health behaviours such as healthcare utilisation, antenatal care visits, and safer sex negotiation. However, very few studies explore how household structure (i.e., monogamous or polygamous) is associated with married women’s household decision-making autonomy. Our paper seeks to address this gap. Using the 2019–20 Mauritania Demographic and Health Survey, a nationally representative dataset, and applying logistic regression analysis, we explore how married women’s household structure is associated with their household decision-making autonomy. We find that 9% of married women are in polygamous marriages, while 63% and 65% are involved in decision-making about their health and large household purchases, respectively. Additionally, 76% and 56% are involved in decision-making about visiting family or relatives and household expenditures. After accounting for socio-economic and demographic factors, we find that compared to women from monogamous households, those from polygamous households are less likely to participate in decision-making about their health (OR=0.65, p < 0.001), making large household purchases (OR=0.65, p < 0.001), visiting family or relatives (OR=0.72, p < 0.001), and household expenditure (OR=0.58, p < 0.001). Based on our findings, we recommend the urgent need to review and re-evaluate policies and approaches seeking to promote gender equality and women’s autonomy in Mauritania. Specifically, it may be critical for intervention programmes to work around reducing power imbalances in polygamous household structures that continue to impact married women’s household decision-making autonomy adversely. Such interventions should centre married women’s socio-economic status as a central component of their empowerment strategies in Mauritania.

We extend the work of Ivancovsky et al. by proposing that in addition to novelty seeking, mood regulation goals – including enhancing positive mood and repairing negative mood – motivate both creativity and curiosity. Additionally, we discuss how the effects of mood on state of mind are context-dependent (not fixed), and how such flexibility may impact creativity and curiosity.

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 electrochemical properties of kaolinite before and after modification with chlorodimethyl-octadecylsilane have been studied by electrophoretic mobility, surface charge titration, and extrapolated yield stress measurements as a function of pH and ionic strength. A heteropolar model of kaolinite, which views the particles as having a pH-independent permanent negative charge on the basal planes and a pH-dependent charge on the edges, has been used to model the data. The zeta potential and surface charge titration experimental data have been used simultaneously to calculate acid and ion complexation equilibrium constants using a surface complex model of the oxide-solution interface. The experimental data were modeled following subtraction of the basal plane constant negative charge, describing only the edge electrical double layer properties. Extrapolated yield stress measurements along with the electrochemical data were used to determine the edge isoelectric points for both the unmodified and modified kaolinite and were found to occur at pH values of 5.25 and 6.75, respectively. Acidity and ion complexation constants were calculated for both sets of data before and after surface modification. The acidity constants, pKa1 = 5.0 and pKa2 = 6.0, calculated for unmodified kaolinite, correlate closely with acidity constants determined by oxide studies for acidic sites on alumina and silica, respectively, and were, therefore, assigned to pH-dependent specific chemical surface hydroxyl groups on the edges of kaolinite. The parameters calculated for the modified kaolinite indicate that the silane has reacted with these pH-dependent hydroxyl groups causing both a change in their acidity and a concomitant decrease in their ionization capacity. Infrared data show that the long chain hydrocarbon silane is held by strong bonding to the kaolinite surface as it remains attached after washing with cyclohexane, heating, and dispersion in an aqueous environment.

Empowering the Participant Voice (EPV) is an NCATS-funded six-CTSA collaboration to develop, demonstrate, and disseminate a low-cost infrastructure for collecting timely feedback from research participants, fostering trust, and providing data for improving clinical translational research. EPV leverages the validated Research Participant Perception Survey (RPPS) and the popular REDCap electronic data-capture platform. This report describes the development of infrastructure designed to overcome identified institutional barriers to routinely collecting participant feedback using RPPS and demonstration use cases. Sites engaged local stakeholders iteratively, incorporating feedback about anticipated value and potential concerns into project design. The team defined common standards and operations, developed software, and produced a detailed planning and implementation Guide. By May 2023, 2,575 participants diverse in age, race, ethnicity, and sex had responded to approximately 13,850 survey invitations (18.6%); 29% of responses included free-text comments. EPV infrastructure enabled sites to routinely access local and multi-site research participant experience data on an interactive analytics dashboard. The EPV learning collaborative continues to test initiatives to improve survey reach and optimize infrastructure and process. Broad uptake of EPV will expand the evidence base, enable hypothesis generation, and drive research-on-research locally and nationally to enhance the clinical research enterprise.

Dicamba and 2,4-D are postemergence herbicides widely used to control broadleaf weed species in crop and non-crop areas in the United States. Currently, multiple formulations of 2,4-D and dicamba are available on the market. Even though the active ingredient is the same, the chemical formulation may vary, which can influence the volatility potential of these herbicides. Therefore, the objective of this study was to evaluate the response of soybean, cotton, and tobacco plants exposed to vapors of 2,4-D and dicamba formulations alone or mixed in humidomes for 24 h. Humidome studies were conducted in an open pavilion at the Lake Wheeler Turfgrass Field Lab of the North Carolina State University in Raleigh, NC. Dicamba and mixture treatments injured and caused a reduction in the height of soybean. Injury varied from 55% to 70%, and average plant height was 8.8 cm shorter compared with untreated control plants. Treatments with 2,4-D caused the least injury to soybean (≤21%), and differences among formulations were identified (dimethylamine > choline > dimethylamine-monomethylamine). However, soybean height was not affected by 2,4-D treatments. No differences between treatments were observed when herbicides were applied to cotton. The greatest injury to tobacco (23.3%) was caused by dicamba dimethylamine. Overall, the effect of 2,4-D and dicamba vapor was species-specific and formulation-dependent. Additionally, environmental conditions in the humidomes may have played a major role on the outcome of this study.

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.