Knowledge brokers, usually conceptualized as passive intermediaries between scientists and policymakers in evidence-based policymaking, are understudied in the philosophy of science. Here, we challenge that usual conceptualization. As agents in their own right, knowledge brokers have their own goals and incentives, which complicate the effects of their presence at the science–policy interface.We illustrate this in an agent-based model and suggest several avenues for further exploration of the role of knowledge brokers in evidence-based policy.

Natural phonetic variability such as talker differences facilitates second language (L2) word learning. Whether accent variability enhances the talker variability benefit, particularly in the learning of words that differ only in lexical tones, has not yet been examined despite its potential applied value for L2 tone language learning. Two groups of monolingual English speakers completed six training sessions on four minimal-tone quadrads of Mandarin monosyllabic pseudowords, produced either by 12 talkers from Beijing (single accent) or by four talkers each from Beijing, Yantai, and Guangzhou (multiple accents). Bayesian mixed-effects modeling revealed strong learning improvement across sessions in both conditions, but the multiple-accent group improved faster than the single-accent group. In addition, the multiple-accent group demonstrated superior generalization to new talkers with a familiar and an unfamiliar accent, suggesting that natural L2-Mandarin accent variability facilitates English learners’ access to abstract tone-word lexical representations, beyond the benefits of talker variability alone.

Objectives/Goals: To discover novel phenogroups of atrial fibrillation (AF) from a real-world claims database using a multi-algorithm unsupervised machine learning (ML) approach and to reveal clinical factors associated with each phenogroup using supervised ML. We aim to establish clinically meaningful AF phenogroups to advance precision medicine. Methods/Study Population: We will leverage the Merative MarketScan databases (2016–2024) of real-world claims data with an estimated >600,000 AF patients. Four unsupervised ML clustering techniques will be employed to identify phenogroups: k-means, Gaussian mixture models, agglomerative, and HDBSCAN. Following cluster generation, the algorithms will be evaluated through internal validation via the Davies–Bouldin Index which calculates cluster compactness and separation, and the Adjusted Rand Index to determine stability between algorithms. Then, supervised ML via SuperLearner, which will include advanced algorithms (e.g., gradient boosting machines and support vector machines), will identify clinical factors associated with each phenogroup and will be evaluated using k-fold cross-validation and Shapley values. Results/Anticipated Results: We anticipate that the multi-algorithm approach will yield robust, distinct, and clinically meaningful AF phenogroups. SuperLearner will confirm the key clinical characteristics driving membership in each group (e.g., hypertension and heart failure). These data-driven classifications will provide a robust foundation for subsequent translational analyses. We will perform time-to-event analyses to assess the differential risks of stroke, coronary artery disease and myocardial infarction, heart failure, and rates of healthcare utilization across phenogroups. Next, we will employ the target trial framework to evaluate the comparative effectiveness of anticoagulant therapies on stroke incidence across phenogroups. Discussion/Significance of Impact: This work demonstrates a highly rigorous, multi-algorithm approach to characterize a large, real-world database of AF patients. By constructing novel AF phenogroups, we aim to move beyond the limitations of current AF classifications and advance precision medicine by providing the foundation for individualized therapeutic strategies.

Maternal tobacco smoking during pregnancy (MSDP) is associated with an increased risk of child externalizing problems. It remains unclear whether these associations are externalizing-specific, or better explained by comorbidity between externalizing and internalizing domains, or vary by age and sex. To address comorbidity and differentiation between domains, we leveraged the severity-directional model of psychopathology. Severity reflects the overall level of psychopathology symptoms across both domains (high levels of severity can only be reached by having symptoms of both types simultaneously), whereas directionality captures the balance/differentiation of internalizing vs. externalizing symptoms regardless of number of total symptoms. Participants included 16,335 children aged 1–19 years old (47.78% female, 58.17% White, 75.46% non-Hispanic) from 55 U.S.-based cohorts within the Environmental Influences on Child Health Outcomes (ECHO) consortium. MSDP predicted differentiation toward externalizing problems in 2-year age bins 1–2 through 7–8 and 13–14 years; remaining (non-significant) age bins had similar magnitudes. MSDP predicted higher symptom severity in all age bins. Findings likely reflect a combination of MSDP associations with comorbid symptom severity and specificity toward externalizing problems, with little evidence of age or sex differences. Additional analyses explored e-cigarette use, other prenatal substance use, and postnatal smoke exposure; associations were sparse and unsystematic.

Prior research on technology entrepreneurship has been grounded almost exclusively in capitalist frameworks developed in the Global North. We argue that context matters, and that scholars should examine technology entrepreneurship in the roughly half of the world where socialism provides the economic foundation. As a step in this direction, we investigate how the principle of common prosperity shapes technology entrepreneurship in socialist contexts. Focusing first on China – a global technological leader and one of the world’s largest generators of patents – we show through two cases that concern for the poor is not incidental but integral to technology entrepreneurship under socialism. We then extend the analysis to a second socialist context outside China, demonstrating the broader relevance of common prosperity for understanding the relationship between technology entrepreneurship and poverty alleviation. Taken together, these cases suggest that research on technology entrepreneurship should move beyond Global North capitalist assumptions and instead account for societal context, particularly the socialist emphasis on common prosperity. Incorporating such perspectives invites scholars to reconsider the role of technology and entrepreneurship in advancing the common good and reducing poverty.

In the process of utilising machine vision-assisted large aircraft component docking assembly, due to the occlusion induced by process equipment such as assembly tooling, the features on the calibration board cannot be extracted by each camera at the same time, resulting in calibration difficulties or calibration failure. This paper aims to propose a stereo calibration method for multi-cameras in large aircraft component assembly to improve calibration accuracy. Firstly, the sub-pixel edge extraction method based on Canny-Zernike is proposed to accurately extract the circular edges and circle centres of the calibration board, and the Zernike moment model is improved. The circle centre sorting method based on the triangular markers is introduced to realise the sorting of circle centres on the calibration board. Secondly, the intrinsic and extrinsic parameter models of multi-cameras and the visual parameter models between cameras are constructed, and Zhang’s calibration method and indirect calibration method are integrated to solve the parameters. Subsequently, the distortion correction model is optimised by Levenberg-Marquardt. Finally, experiments are performed to test the proposed method. The results show that the proposed method, compared with uncalibration and Zhang’s calibration method, the proposed method achieves stereo calibration of the multi-cameras under complex working conditions, enhances the calibration accuracy and improves the quality of the large aircraft component docking assembly.

To investigate the advantages and disadvantages of two multi-swirl fuel-rich dome configurations, namely the triple-swirler and double-swirler, for a novel high-temperature rise centre-staged combustor, this study employed ANSYS Fluent software. Utilising the Reynolds-averaged Navier-Stokes (RANS) equation as the governing equation, three-dimensional numerical simulations were conducted using the Realisable k-ε turbulence model and non-premixed probability density function (PDF) combustion model to analyse the flow and combustion characteristics of both configurations. A comparative study was then performed to evaluate the performance differences between the two dome configurations under take-off and idle conditions. The results demonstrate that, under both conditions, the fuel-air mixing in the triple-swirler combustor occurs faster and more uniformly. Specifically, during takeoff, the primary zone temperature distribution in the triple-swirler combustor is more uniform, while during idle, the fuel-rich combustion region is more symmetrical. Furthermore, across both conditions, the outlet temperature distribution of the triple-swirler combustor is of superior quality, albeit with equivalent combustion efficiency. Notably, the formation of NOx and soot in the triple-swirler combustor, during takeoff conditions, exceeds that of the double-stage combustor along the flow path, whereas the generation of CO and UHC, during idle conditions, is lower in the former.

Guided by steeling and hormesis models, this paper examined parenting adversity as a quadratic predictor of children’s emotion knowledge and effortful control and, in turn, their internalizing and externalizing symptoms. Participants were 238 mothers, partners, and their preschool children (Mage = 4.38 years; 52% female). Multiple methods (i.e., observations, interviews, surveys, q-sorts) and informants (i.e., trained observers, experimenters, mothers, children, teachers) were used in a longitudinal design with three annual measurement occasions. Supporting the first link in the mediational cascade, lagged, autoregressive analyses indicated that a quadratic composite of parenting adversity derived from trained observer ratings of parenting at Wave 1 was a significant predictor of children’s emotion knowledge and effortful control at Wave 2. In the second part of the proposed cascade, children’s Wave 2 emotion knowledge predicted lower levels of their Wave 3 internalizing symptoms, while their Wave 2 effortful control predicted lower levels of their Wave 3 externalizing symptoms. Consistent with steeling effects, curvilinear findings in the first part of the cascade indicated that moderate levels of exposure to parenting adversity predicted the highest levels of children’s subsequent emotion knowledge and effortful control. Children also exhibited substantially diminished emotion knowledge and effortful control as their exposure to family adversity increased from moderate to high levels.

A new phylogenetic analysis of Lecanora s. lat. is presented based on a dataset of seven loci, including recently published mitochondrial markers. In this analysis, comprising 136 specimens and 572 sequences, several clades that can be circumscribed taxonomically are recovered as monophyletic with strong support. Lecanora divides into two large monophyletic subclades. The well-supported MPRPS clade sensu Medeiros et al. (2021) combines the genera Lecanoropsis, Myriolecis, Protoparmeliopsis, Rhizoplaca and the Lecanora polytropa- and L. varia-groups. The second unsupported clade contains the genera Bryonora, Palicella, Pulvinora and Vainionora, plus the Lecanora albella/subcarnea-, L. carpinea/rupicola-, L. intumescens-, L. subfusca- and L. symmicta-groups. Japewia, Lecidella, Miriquidica, Ramboldia and members of the Lecanora fuscescens-group are placed outside these two clades. Phylogenetic, morphological and chemical evidence supports the resurrection of three genera: Glaucomaria (for the Lecanora carpinea/rupicola-group), Straminella (for the L. varia-group), and Zeora (for the L. symmicta-group). Descriptions for each of the resurrected genera are provided, including new taxonomic combinations and lists of additional Lecanora species likely to be transferred to the proposed new genera pending further studies. Comments on the nomenclature of the new genera, as well as for Lecanoropsis and Myriolecis, are provided.

The dhole Cuon alpinus is a large canid that is categorized as Endangered on the IUCN Red List and at risk of global extinction. Information on the spatial distribution of suitable habitat is important for conservation planning but is largely unavailable. We quantified the spatial distribution of potential range as well as the relative probability of dhole occurrence across large parts of the species’ global range. We used the MaxEnt algorithm to produce a multi-scale environmental niche model based on 24 environmental variables and dhole occurrence data from 12 countries. We identified three regions where dhole conservation should be focused: western India, central India, and across the Himalayan foothills through Southeast Asia. Connectivity between suitable areas was poor, so coordinated action among these regions should be a priority. For instance, transboundary dhole conservation initiatives across the Himalayas from southern China, Myanmar, north-east India, Nepal and Bhutan need to be initiated. We also highlight the value of improving dhole population viability on unprotected land and increasing monitoring in the northern parts of its historic distribution, in particular in areas within mainland China.

Resolvent-based modelling and estimation is critically dependent on the nonlinear forcing input and hence understanding its role in the flow response is of great significance. This study quantifies the nonlinear forcing input in the resolvent formulation and investigates its characteristics for compressible turbulent boundary layers at Mach number 5.86 and friction Reynolds number 420 subject to adiabatic- and cold-wall conditions. Results show that, with the addition of the eddy viscosity to the resolvent operator, the cross-spectral density (CSD) of the forcing tends to exhibit a spatially uncorrelated distribution, which suggests that the spatial cross-coherence may be neglected and makes the modelling of the forcing input potentially easier. Aiming to quantify the different importance of each forcing component in generating turbulent fluctuations, contributions of the eddy-viscosity-corrected forcing to the flow responses are investigated through reduced-order analysis and matrix decomposition. The streamwise motions are almost insensitive to the temperature-related forcing, and can be oppositely influenced by the wall-normal and spanwise forcing components. By retaining only the diagonal components in the CSD of the forcing input, the assumption of forcing decorrelation in space and among components is also examined in the input–output framework. It is found that this simplified input is able to capture the dominant turbulence features and the local forcing is observed to cause inner-layer responses. That is, present results suggest adequate modelling of the CSD of the forcing can be achieved retaining only its diagonal components. On the basis of the current findings, the forcing input in the resolvent-based framework is thus modelled, with the wall-normal dependence and amplitude ratio between forcing components designed for compressible turbulent boundary layers. Through an algebraic Lyapunov equation, improved estimations of the statistical spectral densities of velocity and temperature fluctuations are finally obtained, in contrast to the results by simply assuming the forcing CSD to be an identity matrix.

Flight Data Monitoring (FDM) programmes have become a key part of every major airline’s safety management system. They are primarily based on learning from unwanted deviations in flight parameters encountered during normal flight operations. Owing to its unique nature, anomaly detection of FDM presents distinct problem complexities from the majority of analytical and learning tasks. This methodology, while useful, concentrates only on a small part of the operation, leaving most of the data unprocessed, and does not allow for analysing events that had the potential to go wrong but were recovered in time by the crews. This research focused on analysing an FDM dataset of 1332 approaches between January 2018 and July 2022 at Tenerife South Airport (Spain), where there is a known phenomenon of increasing headwinds during the final approach. The flights were clustered using self-organising maps (SOM) by patterns of increasing headwinds, and the clusters were assessed in terms of clustering performance. The clusters were well differentiated. A further comparison between the results from the airline showed that 88 flights were affected by wind shifts, while 27 flights were picked up by the airline. The results demonstrate that SOMs are a meaningful tool for clustering flight data and can complement the current FDM analysis methodology. Combining both methodologies could shift FDM data analysis to look beyond exceedances into what went well, thus shifting the FDM paradigm towards a more safety-II-based method.

One of the most devastating and costly consequences of CM is that it persists across generations. Yet, we know little about whether there is intergenerational continuity of diverse dimensions of CM exposure (e.g., chronicity, multi-subtype) or unique patterns of exposure. This is a critical gap, given evidence that different forms of CM confer unique consequences. To enhance our understanding of intergenerational continuity of CM, the current study applied a multidimensional framework to be the first to investigate whether unique forms of CM exposure (characterized by the subtypes and whether multi-type exposure occurred) exhibited homotypic/heterotypic patterns of intergenerational continuity. Latent class analysis (LCA) was used to identify patterns of CM exposure in mothers and their offspring (aged 8–13) who were part of a high-risk, economically disadvantaged sample of maltreated and nonmaltreated youth (N = 1240). Four distinct classes of CM exposure were identified in both mothers (“Single-Subtype without Sexual Abuse”; “Sexual Abuse”, “Multi-Subtype Exposure”; and “No Maltreatment”) and offspring (“No Maltreatment”; “Single Type-Neglect”; “Single Type-Abuse”; and “Chronic, Multi-type”). Patterns of homotypic and heterotypic intergenerational continuity were identified, with a pattern of multi-type exposure emerging as an enduring form of exposure across generations. Implications for preventive interventions are discussed. Intergenerational continuity of multidimensional patterns of child maltreatment exposure: A person-centered approach

Neuropsychological disorders, including anxiety, depression, and dementia, are significant public health problems among older adults. While psychotropics are effective treatments, long-term treatment often has adverse side effects(1). Many patients often seek healthy food consumption as an alternative preventive strategy. Dietary fibre has been suggested for many health benefits, including cardiometabolic health and anti-inflammation, which may influence neurological health through the gut-brain axis(2). However, fibre’s role in neuropsychological health outcomes in older people is unclear. This study examined the potential role of dietary fibre intake and consumption of fibre-rich foods in neurological health outcomes in older Australians. We utilised data from the Ageing Study (MAS) of 1,037 participants aged 70–90(3). At baseline, dietary fibre, whole grains, fresh fruit, vegetables, and nuts and legumes consumption was estimated using the Cancer Council of Victoria food frequency questionnaire. The intake amount was further derived into tertiles (T), with T1 in the lower 33rd%tile and T3 in the upper 33rd%tile. Depressive symptoms (Geriatric Depression Scale), anxiety symptoms (Goldberg Anxiety Scale), and psychological distress (Kessler Psychological Distress Scale) were assessed. Linear regression models were used to estimate beta coefficients for the associations cross-sectionally. Incident dementia was defined using diagnostic criteria, clinical assessments, and a consensus panel review. Nine hundred and sixty-three participants were followed up from the baseline (2005) until wave 4 (2011) [median: 5.8 (IQR: 3.1–5.9) years; 97 incident cases). Incident depression was defined as diagnoses by healthcare professionals and treatments for depression. Eight hundred and nine participants were followed up from the baseline (2005) until wave 3 (2009) [median: 3.9 (IQR: 1.9–4.0) years; 109 incident cases). Cox proportional hazard models were used to estimate hazard ratios (95% CIs). All models were adjusted for demographic characteristics, lifestyle factors, and health history. Among 963 participants (mean age: 78.5; 5.8% females) in the cross-sectional analysis, compared with T1, higher vegetable intake was associated with fewer depressive symptoms (T2: β = 0.52; T3: β= −0.53; both p < 0.05), psychological distress (T2: β = −0.59; T3: β = −1.13; both p < 0.05), and anxiety symptoms (T3: β = −0.37; p = 0.03). Combined intake of vegetables and fruit was inversely associated with fewer psychological distress symptoms (T2: β = −0.55; p = 0.06; T3: β = −1.3; p < 0.05). In the highest tertile, dietary fibre was associated with fewer depressive symptoms (T3: β = −0.47; p = 0.04). In the longitudinal analysis, dietary fibre intake was associated with a 43–56% lower risk of incident dementia (T2 vs T1: adj.HR = 0.57; 95% CI: 0.31–1.03; T3 vs T1: adj.HR = 0.44; 95% CI: 0.19–1.01). Intakes of whole grains, fruit, nuts and legumes were not associated with the outcomes assessed. In a cohort of older Australians, dietary fibre intake appeared to be protective in reducing depressive symptoms cross-sectionally and the risk of incident dementia longitudinally. Additionally, vegetable consumption was associated with fewer symptoms related to depression, anxiety, and distress cross-sectionally.

Ice shelves affect the stability of ice sheets by supporting the mass balance of ice upstream of the grounding line. Marine ice, formed from supercooled water freezing at the base of ice shelves, contributes to mass gain and affects ice dynamics. Direct measurements of marine ice thickness are rare due to the challenges of borehole drilling. Here we assume hydrostatic equilibrium to estimate marine ice distribution beneath the Amery Ice Shelf (AIS) using meteoric ice-thickness data obtained from radio-echo sounding collected during the Chinese National Antarctic Research Expedition between 2015 and 2019. This is the first mapping of marine ice beneath the AIS in nearly 20 years. Our new estimates of marine ice along two longitudinal bands beneath the northwest AIS are spatially consistent with earlier work but thicker. We also find a marine ice layer exceeding 30 m of thickness in the central ice shelf and patchy refreezing downstream of the grounding line. Thickness differences from prior results may indicate time-variation in basal melting and freezing patterns driven by polynya activity and coastal water intrusions masses under the ice shelf, highlighting that those changes in ice–ocean interaction are impacting ice-shelf stability.