Fluid–structure interaction (FSI) poses a significant computational challenge due to the complex, multiscale nonlinearities of both fluid and structural dynamics. In this study, a novel strongly coupled FSI network is developed for accurate and efficient predictive modelling of FSI problems. Specifically, the framework architecture integrates a physics-constrained convolutional neural network autoencoder (CAE) with a strong coupling (SC) prediction module containing both fluid and structural dynamic prediction modules (DP) that perform recursive prediction simultaneously and interactively. First, the physics-constrained CAE learns low-dimensional nonlinear normal modes (NNMs) representations of the high-dimensional fluid field’s spatiotemporal dynamics. Subsequently, the fluid DP module in the SC module leverages these NNMs combined with structural states determined by embedding the motion equation into the structural DP, to predict the future-state flow fields efficiently. Such a strongly coupled FSI framework is achieved by integrating fluid NNMs and structural states within each time step to recursively correct the learned mapping of the trained CAE and fluid DP modules, thereby efficiently and accurately predicting future-state FSI dynamics simultaneously. The developed SC-CAE-NNM FSI framework is applied to the classic problem of vortex-induced vibrations of a circular cylinder, analysing both laminar and high-$ \textit{Re}$ flows. It is observed that the identified NNMs of fluid flows in association with the structural state achieve superior accuracy in the prediction of the flow fields and structural responses, indicating that the SC scheme effectively captures the dynamic flow characteristics and strong nonlinear interactions. Furthermore, the framework is found to be able to reconstruct small-scale flow structures in high-$ \textit{Re}$ flows accurately and predict structural responses efficiently. Additionally, the analysis of NNMs energy distributions reveals that the majority of the total energy of the flow field is captured by the first four NNMs, demonstrating significant advantages of nonlinear feature representation for efficient reduced-order modelling of complex flows. Overall, this novel framework shows strong capability for accurate and efficient predictive modelling of complex nonlinear dynamics of FSIs.

Meta-analysis is a widely used statistical tool for estimating the diagnostic accuracy of tests across multiple studies. Existing methods and available R packages primarily focus on a single diagnostic test, typically under the assumption that all studies include a gold standard. Greater efficiency can be achieved by modeling multiple diagnostic tests together and drawing on studies with or without a gold standard reference test across diverse designs. To address this challenge, recent work has extended both the Bayesian hierarchical model and the Bayesian hierarchical summary receiver operating characteristic model to the framework of network meta-analysis of diagnostic tests, enabling simultaneous comparison of multiple tests when some data are missing. Despite the importance of these methods, their computational complexity has limited their broad application. This article introduces NMADTA, an R package that implements these models with user-friendly functions. The package allows researchers to evaluate the accuracy of multiple diagnostic tests simultaneously and provides comprehensive graphical displays of the results.

Patent ductus arteriosus requires accurate delineation when non-invasive anatomical definition is needed. We describe a 10-year-old girl in whom photon-counting CT angiography demonstrated a ductus connecting the descending thoracic aorta and proximal left pulmonary artery. Multiplanar and three-dimensional images defined the diameter, length, and spatial relationships. Photon-counting CT may complement echocardiography when detailed anatomical assessment is required.

This paper presents a tri-band flexible wearable monopole antenna integrated with an electromagnetic bandgap (EBG) structure. The antenna uses a flexible PDMS (polydimethylsiloxane) substrate, with resonant frequencies at 2.45, 3.5, and 5.8 GHz, and operates within the frequency bands of 2.42–2.60 GHz, 3.11–3.70 GHz, and 5.32–6.81 GHz. To further optimize the antenna’s impedance matching and radiation characteristics, a defected ground structure is introduced in the design. Additionally, an EBG reflective surface is used to enhance reflection properties, reduce back radiation, improve antenna gain, and decrease the specific absorption rate (SAR). A 4 × 4 tri-band EBG array structure is integrated on the back of the monopole antenna, with each EBG unit consisting of two circular rings and a polygon, achieving zero reflection phase at 2.45, 3.5, and 5.8 GHz. The antenna demonstrates good impedance matching across the designed frequency bands, with a significant gain enhancement of 8.1, 6.64, and 8.21 dBi at the respective frequencies. Simulations with a human model show that the SAR values are below international standards within the operating frequency bands. The antenna also exhibits excellent robustness in its bent state, showing promising potential for applications in medical health monitoring.

Meta-analysis synthesizes evidence from multiple randomized clinical trials and informs evidence-based practices across various medical domains. Recently, causally interpretable meta-analysis has been proposed and applied to treatment evaluations for target populations, requiring individual participant data (IPD). Standard meta-analysis assumes transportability or exchangeability of a (conditional) relative effect (such as relative risk or odds ratio), which may be violated when the relative effects are correlated with the baseline risks across clinical trials. In addition, the weighted average of some study-specific effect measures such as the (log) odds ratios or the (log) hazard ratios is non-collapsible and does not correspond to any target population. Furthermore, when the randomization ratios between treated versus untreated arms vary across trials, confounding bias may occur. To address these challenges, we propose a causal meta-analysis (CMA) framework using only aggregated data, enabling causally interpretable and accurate estimation for different target populations. The CMA adjusts its weights for treatment effect across various target populations, including the average treatment effect (ATE), the ATE on the treated (ATT) population, the ATE on the control (ATC) population, and the ATE in the overlap (ATO) population. Mathematically, we discover the connection between traditional meta-analysis estimators and CMAs. For example, the Mantel–Haenszel weighted meta-analysis is equivalent to the CMA with ATO.

This paper investigates the flow past a flexible splitter plate attached to the rear of a fixed circular cylinder at low Reynolds number 150. A systematic exploration of the plate length ($L/D$), flexibility coefficient ($S^{*}$) and mass ratio ($m^{*}$) reveals new laws and phenomena. The large-amplitude vibration of the structure is attributed to a resonance phenomenon induced by fluid–structure interaction. The modal decomposition indicates that resonance arises from the coupling between the first and second structural modes, where the excitation of the second structural mode plays a critical role. Due to the combined effects of added mass and periodic stiffness variations, the two modes become synchronised, oscillating at the same frequency while maintaining fixed phase difference $\pi /2$. This further results in the resonant frequency being locked at half of the second natural frequency, which is approximately three times the first natural frequency. A reduction in plate length and an increase in mass ratio are both associated with a narrower resonant locking range, while a higher mass ratio also shifts this range towards lower frequencies. A symmetry-breaking bifurcation is observed for cases with $L/D\leqslant 3.5$, whereas for $L/D=4.0$, the flow remains in a steady state with a stationary splitter plate prior to the onset of resonance. For cases with a short flexible plate and a high mass ratio, the shortened resonance interval causes the plate to return to the symmetry-breaking stage after resonance, gradually approaching an equilibrium position determined by the flow field characteristics at high flexibility coefficients.

Indirect treatment comparison (ITC) is widely used to estimate the comparative effectiveness of treatments when head-to-head trials are unavailable. For the typical scenario of anchored ITC where one trial compares drug A to drug C (AC trial) and another compares drug B to drug C (BC trial), the comparative effectiveness of drugs A versus B is calculated by subtracting (or dividing) the relative treatment effect of A versus C in the AC trial by that of B versus C in the BC trial, assuming the covariate distributions in both trials are balanced. This operation is valid only if the chosen effect measure is transitive, that is, in a three-arm randomized trial of drugs A, B, and C, the direct treatment effect of A versus B equals the indirect treatment effect of A versus B through their comparisons to C. For survival outcomes, many ITCs use the hazard ratio (HR) as the effect measure. In this article, we demonstrate that HR is generally not transitive and should be used with caution. As more reliable alternatives, we recommend effect measures with better transitivity properties: the restricted mean survival time (RMST) difference, the landmark survival probability difference (or ratio) at a prespecified time point, and the average hazard with survival weights (AH-SW) difference.

African swine fever (ASF) is a highly contagious animal disease caused by African swine fever virus (ASFV). It is listed by the World Organization for Animal Health (WOAH) as an animal disease subject to statutory reporting. ASFV, a large, enveloped double-stranded DNA virus with high genomic complexity, exhibits a case fatality rate of up to 100%, posing a significant threat to the global pig industry and food safety. To date, the absence of a safe commercial ASFV vaccine primarily stems from challenges in identifying immunogenic viral antigens, insufficient characterization of ASFV pathogenesis, and limited understanding of the virus’s immune evasion mechanisms. Here, we review the pathogenic characteristics (morphological structure, clinical symptoms, and epidemiological characteristics), molecular biological characteristics, and infection mechanism of ASFV, as well as the immune response mechanism, vaccine research, and the latest information on ASFV in other areas. This review will be in favour of understanding the current state of knowledge of ASF and developing effective vaccines to control this disease.

Fully resolving turbulent flows remains challenging due to a turbulent systems’ multiscale complexity. Existing data-driven approaches typically demand expensive retraining for each flow scenario and struggle to generalize beyond their training conditions. Leveraging the universality of small-scale turbulent motions (Kolmogorov’s K41 theory), we propose a scale-oriented zonal generative adversarial network (SoZoGAN) framework for high-fidelity, zero-shot turbulence generation across diverse domains. Unlike conventional methods, SoZoGAN is trained exclusively on a single dataset of moderate-Reynolds-number homogeneous isotropic turbulence (HIT). The framework employs a zonal decomposition strategy, partitioning turbulent snapshots into subdomains based on scale-sensitive physical quantities. Within each subdomain, turbulence is synthesized using scale-indexed models pretrained solely on the HIT database. A SoZoGAN demonstrates high accuracy, cross-domain generalizability and robustness in zero-shot super-resolution of unsteady flows, as validated on untrained HIT, turbulent boundary layer and channel flow. Its strong generalization, demonstrated for homogeneous and inhomogeneous turbulence cases, suggests potential applicability to a wider range of industrial and natural turbulent flows. The scale-oriented zonal framework is architecture-agnostic, readily extending beyond generative adversarial networks to other deep learning models.

We identify a parsimonious set of factors from a large pool of candidates for explaining hedge fund returns, ranging from equity market, anomaly, and trend-following factors to macroeconomic factors. The resulting 9-factor model, including five anomaly factors, outperforms existing hedge fund models both in sample and out of sample, with a significant reduction in alphas while showing substantial cross sectional performance heterogeneity. Further analysis based on fund holdings confirms the model’s ability to capture returns from arbitrage trading. Overall, the anomaly factors help quantify hedge fund strategies and risk exposures and improve fund performance evaluation.

Climate conditions are known to modulate infectious disease transmission, yet their impact on measles transmission remains underexplored. In this study, we investigate the extent to which climate conditions modulate measles transmission, utilizing measles incidence data during 2005–2008 from China. Three climate-forced models were employed: a sinusoidal function, an absolute humidity (AH)-forced model, and an AH and temperature (AH/T)-forced model. These models were integrated into an inference framework consisting of a susceptible–exposed–infectious–recovered (SEIR) model and an iterated filter (IF2) to estimate epidemiological characteristics and assess climate influences on measles transmission. During the study period, measles epidemics peaked in spring in northern China and were more diverse in the south. Our analyses showed that the AH/T model better captured measles epidemic dynamics in northern China, suggesting a combined impact of humidity and temperature on measles transmission. Furthermore, we preliminarily examined the impact of other factors and found that population susceptibility and incidence rate were both positively correlated with migrant worker influx, suggesting that higher susceptibility among migrant workers may sustain measles transmission. Taken together, our study supports a role of humidity and temperature in modulating measles transmission and identifies additional factors in shaping measles epidemic dynamics in China.

Automatic visual localization of electric vehicle (EV) charging ports presents significant challenges in uncertain environments, such as varying surface textures, reflections, lighting and observation distance. Existing methods require extensive real-world training data and well-focused images to achieve robust and accurate localization. However, both requirements are difficult to meet under variable and unpredictable conditions. This paper proposes a 2-stage vision-based localization approach. Firstly, the image synthesis technique is used to reduce the cost of real-world data collection. A task-oriented parameterization protocol (TOPP) is proposed to optimize the quality of the synthetic images. Secondly, an autofocus and servoing strategy is proposed. A hybrid detector is employed to enhance sharpness assessment performance, while a visual servoing method based on single exponential smoothing (SES) is developed to enhance stability and efficiency during the search process. Experiments were conducted to evaluate image synthesis efficiency, detection accuracy, and servoing performance. The proposed method achieved 99% detection accuracy on the real-world port images, and guided the robot to the optimal imaging position within 16 s, outperforming comparable approaches. These results highlight its potential for robust automated charging in real-world scenarios.

Aims: Antenatal depression significantly impacts maternal and foetal health outcomes, yet it remains underdiagnosed and undertreated. The Psychological Resilience in Antenatal Management (PRAM) programme at KK Women’s and Children’s Hospital in Singapore was established in December 2022 as a strategy to identify antenatal depression early among pregnant patients. Under the PRAM programme, universal antenatal depression screening is integrated into the routine care programme for pregnant patients, using a modified version of the Edinburgh Postnatal Depression Scale (EPDS) questionnaire during their routine obstetric check-up in the second trimester, for early intervention by the perinatal mental health team.

This qualitative study explores the lived experiences of pregnant women who have undergone screening and intervention under the PRAM programme. It seeks to understand their perceptions of the screening and intervention process, identify barriers and facilitators to help-seeking, and examine effective components of the therapeutic process.

Methods: Using an Interpretative Phenomenological Analysis (IPA) approach, semi-structured interviews were conducted with 10 women who have participated in the PRAM programme between November 2023 to January 2025. Interviews were completed either virtually over Zoom (N=8) or in person (N=2). The interviews explored participants’ experiences with antenatal depression screening, subsequent interventions, and their overall pregnancy journey while managing mental health concerns.

Results: Preliminary analysis reveals several key themes in participants’ experiences. For half of the participants (N=5), the screening process served as an opportunity for self-evaluation and mental health awareness. Obstetricians have also been identified to be crucial facilitators, serving as the initial point of psychiatric referral and influencing women’s decisions to seek support. A significant barrier identified by four participants was the stigma associated with psychiatric diagnoses and receiving psychiatric help. Additionally, participants emphasised the importance of spousal involvement in the therapeutic process, with several women expressing a desire for greater partner participation in their mental health journey.

Conclusion: Understanding women’s experiences with the PRAM programme contributes to improving screening protocols and developing more effective, patient-centred approaches to managing antenatal depression. The findings highlight the need for integrated care pathways that address stigma, enhance partner involvement, and strengthen the role of obstetricians in perinatal mental health care. These insights can inform the development of more comprehensive and accessible mental health support services within perinatal care settings.

We present a high-power mid-infrared single-frequency pulsed fiber laser (SFPFL) with a tunable wavelength range from 2712.3 to 2793.2 nm. The single-frequency operation is achieved through a compound cavity design that incorporates a germanium etalon and a diffraction grating, resulting in an exceptionally narrow seed linewidth of approximately 780 kHz. Employing a master oscillator power amplifier configuration, we attain a maximum average output power of 2.6 W at 2789.4 nm, with a pulse repetition rate of 173 kHz, a pulse energy of 15 μJ and a narrow linewidth of approximately 850 kHz. This achievement underscores the potential of the mid-infrared SFPFL system for applications requiring high coherence and high power, such as high-resolution molecular spectroscopy, precision chemical identification and nonlinear frequency conversion.