Hand, foot, and mouth disease (HFMD) shows spatiotemporal heterogeneity in China. A spatiotemporal filtering model was constructed and applied to HFMD data to explore the underlying spatiotemporal structure of the disease and determine the impact of different spatiotemporal weight matrices on the results. HFMD cases and covariate data in East China were collected between 2009 and 2015. The different spatiotemporal weight matrices formed by Rook, K-nearest neighbour (KNN; K = 1), distance, and second-order spatial weight matrices (SO-SWM) with first-order temporal weight matrices in contemporaneous and lagged forms were decomposed, and spatiotemporal filtering model was constructed by selecting eigenvectors according to MC and the AIC. We used MI, standard deviation of the regression coefficients, and five indices (AIC, BIC, DIC, R2, and MSE) to compare the spatiotemporal filtering model with a Bayesian spatiotemporal model. The eigenvectors effectively removed spatial correlation in the model residuals (Moran’s I < 0.2, p > 0.05). The Bayesian spatiotemporal model’s Rook weight matrix outperformed others. The spatiotemporal filtering model with SO-SWM was superior, as shown by lower AIC (92,029.60), BIC (92,681.20), and MSE (418,022.7) values, and higher R2 (0.56) value. All spatiotemporal contemporaneous structures outperformed the lagged structures. Additionally, eigenvector maps from the Rook and SO-SWM closely resembled incidence patterns of HFMD.

Objectives/Goals: Sexual minority populations (SMPs), including lesbian, gay, and bisexual groups, disproportionately encounter discriminatory experiences due to bi/homonegativity and systemic inequities across various social domains. We aim to understand how the neighborhood-level stressors and resilience sources differed across specific groups in SMPs. Methods/Study Population: Utilizing the NIH All of Us’ cloud-based platform, we selected cohorts self-identifying as gay (n  =  9,454), bisexual (n  =  15,284), lesbian (n = 5267), or straight (n  =  349,748). We explored multiple key measures of neighborhood-level stressors (e.g., neighborhood disorder, neighborhood cohesion, and environment index) and resilience sources (e.g., neighbor cohesion, social support), and other factors (e.g., food insecurity, housing insecurity, and housing instability) by their sexual orientations using analysis of variance or Chi-square analyses. Results/Anticipated Results: Our sample comprised 60.8% females and 37.5% males identifying as non-binary or transgender, with an average age of 55.6 years (SD  =  17.1). The racial composition was 56.0% White, 19.4% Black, 18.7% Hispanic, and 5.9% others (e.g., Asian, multiracial). Compared to straight individuals, SMPs reported high neighborhood stressors (e.g., disorder, worse environment) but lower neighborhood-level resilience sources (e.g., social support, cohesion). In addition, bisexual groups reported highest prevalence of housing insecurity (6.7% vs. 2.3%), housing instability (36.0% vs. 19.6%), and food insecurity (26.57% vs. 12.21%). Discussion/Significance of Impact: SMPs, particularly bisexual individuals, face greater neighborhood stressors and fewer resilience sources than their straight counterparts. These findings call for targeted interventions to address these disparities and promote health equity, using large-scale datasets to inform community-based solutions.

Objectives/Goals: Discriminatory experiences within healthcare settings significantly hinder equitable health access for sexual minority groups (SMPs) in the USA. These discriminatory experiences can manifest in various forms (e.g., refusal of care). We aimed to explore different types of discrimination encountered by SMPs in the healthcare settings. Methods/Study Population: This study utilized secondary data from the NIH All of Us Research Program. For this analysis, we selected cohorts self-identifying as gay (n  =  9,454), bisexual (n  =  15,284), lesbian (n  =  5,267), and straight (n  =  349,748), enabling robust comparisons across SMPs and straight individuals. We employed analysis of variance and Chi-square analyses to assess group differences in healthcare discrimination, using key indicators from the Discrimination in Medical Settings Scale. These indicators captured experiences such as being treated with less respect or courtesy and feeling ignored by healthcare providers, providing a comprehensive view of discriminatory encounters in healthcare settings for SMPs. Results/Anticipated Results: Our analyses revealed that bisexual individuals reported the highest levels of healthcare discrimination (mean  =  3.64, SD  =  2.45), followed by lesbians (mean  =  3.37, SD  =  2.47), other SMPs (mean  =  3.36, SD  =  2.53), gay (mean  =  2.69, SD  =  2.47), and straight participants (mean  =  2.60, SD  =  2.42). Among the seven discrimination indicators, the most reported experience was feeling like a doctor or nurse was not listening, with 76.8% of bisexual participants, 72.3% of lesbians, 68.8% of other SMPs, and 56.9% of gay participants reporting this experience. This was followed by reports of being treated with less respect and being treated with less courtesy in healthcare settings. These findings highlight the pervasive nature of healthcare discrimination among SMPs, particularly bisexual individuals. Discussion/Significance of Impact: SMPs experience higher levels of discrimination in healthcare settings compared to their straight counterparts. Our results underscore the urgent need to foster respectful, inclusive healthcare environments and ensure that healthcare providers are adequately trained to address the unique health needs and experiences of SMPs.

Objectives/Goals: Sexual minority populations report a disproportionately high prevalence of alcohol use, often attributed to coping with bi/homonegativity and systemic inequities across various social domains. This study aims to explore alcohol use patterns and associated neighborhood and individual factors among sexual minority populations (SMPs) using data from the NIH All of US dataset. Methods/Study Population: Alcohol use was assessed using the AUDIT-C (Alcohol Use Disorders Identification Test—Consumption) scale across a sample of 9,454 gay, 15,284 bisexual, 5,267 lesbian, and 349,748 straight participants. The AUDIT-C measured hazardous alcohol use, and logistic regression models were employed to examine its association with neighborhood-level factors (e.g., socioeconomic status, alcohol outlet density) and individual-level factors (e.g., age, race/ethnicity, income, and education) among SMPs. Interaction terms assessed how these relationships varied by sexual orientation. Sensitivity analyses were conducted to assess the robustness of the findings, including stratified analyses by gender identity and the exclusion of extreme outliers in alcohol use reporting. Results/Anticipated Results: Our analyses revealed that gay participants had the highest AUDIT-C scores (mean  =  3.60, SD  =  2.27), followed by bisexual (mean  =  3.35, SD  =  2.21), other SMPs (mean  =  3.18, SD  =  2.19), lesbian (mean  =  3.04, SD  =  2.08), and straight individuals (mean  =  3.05, SD  =  2.06). Alcohol use was positively associated with neighborhood disorder (β  =  0.12, 95% CI  =  0.07, 0.17), housing insecurity (β  =  0.14, 95% CI  =  0.03, 0.25), and male gender (β  =  0.98, 95% CI  =  0.96, 1.00). In contrast, neighborhood density (β  =  -0.11, 95% CI  =  -0.15, -0.07), food insecurity (β  =  -0.14, 95% CI  =  -0.20, -0.08), being Black, and identifying as bisexual were negatively associated with alcohol use. Sensitivity analyses determined no significant differences among specfic supgroups. Discussion/Significance of Impact: This study highlights important differences in alcohol use across SMPs and emphasizes the influence of neighborhood-level stressors (e.g., disorder and housing insecurity). These findings underscore the need for addressing social and environmental determinants of alcohol use in SMPs to mitigate the negative impacts of alcohol consumption.

The dynamic model of the distributed propulsion vehicle faces significant challenges due to several factors. The primary difficulties arise from the strong coupling between multiple power units and aerodynamic rudder surfaces, the interaction between thrust and vehicle dynamics, and the complexity of the aerodynamic model, which includes high-dimensional and high-order variables. To address these challenges, wind tunnel tests are conducted to analyse the aerodynamic characteristics and identify variables affecting the aerodynamic coefficients. Subsequently, a deep neural network is employed to investigate the influence of the power system and aerodynamic rudder on the aerodynamic coefficients. Based on these findings, a multi-dynamic coupled aerodynamic model is developed. Furthermore, a control-oriented nonlinear dynamics model for the distributed propulsion vehicle is established, and a flight controller is designed. Finally, closed-loop simulations for the climb, descent and turn phases are performed, validating the effectiveness of the established model.

Machine learning (ML) models have been developed to identify randomised controlled trials (RCTs) to accelerate systematic reviews (SRs). However, their use has been limited due to concerns about their performance and practical benefits. We developed a high-recall ensemble learning model using Cochrane RCT data to enhance the identification of RCTs for rapid title and abstract screening in SRs and evaluated the model externally with our annotated RCT datasets. Additionally, we assessed the practical impact in terms of labour time savings and recall improvement under two scenarios: ML-assisted double screening (where ML and one reviewer screened all citations in parallel) and ML-assisted stepwise screening (where ML flagged all potential RCTs, and at least two reviewers subsequently filtered the flagged citations). Our model achieved twice the precision compared to the existing SVM model while maintaining a recall of 0.99 in both internal and external tests. In a practical evaluation with ML-assisted double screening, our model led to significant labour time savings (average 45.4%) and improved recall (average 0.998 compared to 0.919 for a single reviewer). In ML-assisted stepwise screening, the model performed similarly to standard manual screening but with average labour time savings of 74.4%. In conclusion, compared with existing methods, the proposed model can reduce workload while maintaining comparable recall when identifying RCTs during the title and abstract screening stages, thereby accelerating SRs. We propose practical recommendations to effectively apply ML-assisted manual screening when conducting SRs, depending on reviewer availability (ML-assisted double screening) or time constraints (ML-assisted stepwise screening).

A three-dimensional robust nonlinear cooperative guidance law is proposed to address the challenge of multiple missiles intercepting manoeuvering targets under stringent input constraints and thruster failure. The finite-time convergence theory is used to design a distributed nonlinear sliding mode guidance law, ensuring that the system converges in finite time, with the upper limit of convergence time related to the initial state. A nonlinear sliding surface is adopted to mitigate actuator saturation issues. Then, considering thruster failure, a robust cooperative guidance law is further introduced, ensuring mission completion through the reconstruction of the guidance law. The closed-loop system is proven to be stable using Lyapunov theory, and the influence of hyperparameters on the cooperative guidance law is analysed. Additionally, the results of numerical simulations and hardware-in-the-loop experiments demonstrate the effectiveness and robustness of the proposed algorithm in dealing with stringent input saturation and various disturbances.

Network meta-analysis (NMA), also known as mixed treatment comparison meta-analysis or multiple treatments meta-analysis, extends conventional pairwise meta-analysis by simultaneously synthesizing multiple interventions in a single integrated analysis. Despite the growing popularity of NMA within comparative effectiveness research, it comes with potential challenges. For example, within-study correlations among treatment comparisons are rarely reported in the published literature. Yet, these correlations are pivotal for valid statistical inference. As demonstrated in earlier studies, ignoring these correlations can inflate mean squared errors of the resulting point estimates and lead to inaccurate standard error estimates. This article introduces a composite likelihood-based approach that ensures accurate statistical inference without requiring knowledge of the within-study correlations. The proposed method is computationally robust and efficient, with substantially reduced computational time compared to the state-of-the-science methods implemented in R packages. The proposed method was evaluated through extensive simulations and applied to two important applications including an NMA comparing interventions for primary open-angle glaucoma, and another comparing treatments for chronic prostatitis and chronic pelvic pain syndrome.

This study aimed to investigate the intake of dairy products during pregnancy in women with gestational diabetes mellitus (GDM) and its impacts on neonatal birth weight and pregnancy outcomes. A total of 386 women with GDM during the second trimester pregnancy participated in this prospective cohort study. We evaluated dairy products intake through the FFQ. Pregnancy outcomes were obtained from the delivery data. Participants were divided into insufficient and sufficient intake of milk and dairy products groups (< 300 g/d and ≥ 300 g/d, respectively). The average intake of dairy products during the second trimester pregnancy in women with GDM was 317·8 ± 179·5 g/d, and the total energy intake was 1635·4 ± 708·7 kcal/d. However, 76·68 % of them did not meet the recommended total energy intake of women with GDM. After adjusting for confounding factors, women with GDM who consumed ≥ 300 g/d of dairy products had an average reduction in birth weight of 93·1 g compared with women who consumed < 300 g/d of dairy products (95 % CI −171·343, −14·927). Women with GDM in sufficient intake group was also associated with lower risk of macrosomia (95 % CI 0·043, 0·695) and caesarean section (95 % CI 0·387, 0·933) and not related to low birth weight infant (95 % CI 0·617, 14·502) and preterm birth (95 % CI 0·186, 1·510) when compared with participants in insufficient intake group. Under the premise of insufficient total energy intake, the intake of dairy products during the second trimester pregnancy in women with GDM might be related to the decrease of neonatal birth weight.

Electron cyclotron resonance ion thrusters (ECRITs) have the potential to be used for space gravitational wave detection due to their wide thrust range. However, an unclear understanding of dynamic processes of ECRITs with strongly coupled multi-operating parameters limits further improvements on thrust noise and response velocity by feedback control systems. An integrative mathematical model considering the non-Maxwell electron energy distribution function for ECRITs is validated by experiments and used to study the influence of operating parameters on the dynamic processes of thrusters, which provides a new simplified grid model. Simulation results show the response processes with microwave (MW) power can be divided into two stages. The characteristic times of the first and second stages are respectively several microseconds and 10 ms, which are respectively dominated by plasma motion and the volume effect. The overshoot of screen grid (SG) current decreases, while its response time remains unchanged when the response time of MW power is prolonged. The response time of SG current with a step increase of flow rate is approximately 10 ms, consistent with the volume effect. The SG current decreases with rise of flow rate for high flow rate operations due to the small increment of ion density limited by low electron temperature, the decrease of ion Bohm velocity and reduction of sheath extraction area. The influence of grid voltage on the dynamic process of the SG current depends on variation ranges of extraction capabilities. When variations of sheath extraction area are limited, the response time is 5 μs, consistent with plasma response time. It is prolonged to 0.5 ms if sheath extraction area variations are large because they cause obvious variations of plasma parameters in the discharge chamber. These dynamic results can not only facilitate designing feedback controllers of micro-propulsion systems for high-precision space missions, but also provide guidance for ion sources to generate highly stable or rapid-response ion beam.

Phylogenetic analysis demonstrates that Kuamaia lata, a helmetiid euarthropod from the lower Cambrian (Series 2, Stage 3) Chengjiang Konservat-Lagerstätte, nests robustly within Artiopoda, the euarthropod clade including trilobitomorphs. Microtomography of new specimens of K. lata reveals details of morphology, notably a six-segmented head and raptorial frontal appendages, the latter contrasting with filiform antennae considered to be a diagnostic character of Artiopoda. Phylogenetic analyses demonstrate that a raptorial frontal appendage is a symplesiomorphy for upper stem-group euarthropods, retained across a swathe of tree space, but evolved secondarily in K. lata from an antenna within Artiopoda. The phylogenetic position of K. lata adds support to a six-segmented head being an ancestral state for upper stem- and crown-group euarthropods.

Rhopalosiphum padi is an important grain pest, causing severe losses during crop production. As a systemic insecticide, flonicamid can control piercing-sucking pests efficiently. In our study, the lethal effects of flonicamid on the biological traits of R. padi were investigated via a life table approach. Flonicamid is highly efficiently toxic to R. padi, with an LC50 of 9.068 mg L−1. The adult longevity and fecundity of the R. padi F0 generation were markedly reduced under the LC25 and LC50 concentrations of flonicamid exposure. In addition, negative transgenerational effects on R. padi were observed under exposure to lethal concentrations of flonicamid, with noticeable decreases in the reproductive period, adult longevity, total longevity, and total fecundity of the F1 generation under the LC25 concentration of flonicamid. Furthermore, the third nymph stage (N3), preadult stage, duration of the adult pre-reproductive period, duration of the total pre-reproductive period, reproductive period, adult longevity, total longevity, and total fecundity of the F1 generation were significantly lower under treatment with the LC50 concentration of flonicamid. The life table parameters were subsequently analysed, revealing that the intrinsic rate of increase (rm) and the net reproductive rate (R0) were significantly lower but that the finite rate of increase (λ) and the mean generation time (T) were not significantly different under the LC25 and LC50 concentrations of flonicamid. These data are beneficial for grain aphid control and are critical for exploring the role of flonicamid in the integrated management of this key pest.

In this article, we establish a function field analog of Jacobi’s theorem on sums of squares and analyze its moments. Our approach involves employing two distinct techniques to derive the main results concerning asymptotic formulas for the moments. The first technique utilizes Dirichlet series framework to derive asymptotic formulas in the limit of large finite fields, specifically when the characteristic of $\mathbb {F}_q[T]$ becomes large. The second technique involves effectively partitioning the set of polynomials of a fixed degree, providing asymptotic formulas in the limit of large polynomial degree.

We study scaled topological entropy, scaled measure entropy, and scaled local entropy in the context of amenable group actions. In particular, a variational principle is established.

This paper proposes an online robust self-learning terminal sliding mode control (RS-TSMC) with stability guarantee for balancing control of reaction wheel bicycle robots (RWBR) under model uncertainties and disturbances, which improves the balancing control performance of RWBR by optimising the constrained output of TSMC. The TSMC is designed for a second-order mathematical model of RWBR. Then robust adaptive dynamic programming based on an actor-critic algorithm is used to optimise the TSMC only by data sampled online. The system closed-loop stability and convergence of the neural network weights are guaranteed based on the Lyapunov analysis. The effectiveness of the proposed algorithm is demonstrated through simulations and experiments.