Systematic experimental data on local extreme velocities and unsteady features in high-Reynolds-number deep-cavity flows remain limited. In this study, two-component particle image velocimetry (PIV) measurements were performed to investigate the flow characteristics of an incoming-flow-driven deep cavity with $L/D$ = $1/3$ at Re = 2.55 × 105. Three streamwise $xy$ planes ($z/W$ = 0.10, 0.25, 0.50) and three spanwise yz planes ($x/L$ = 0.10, 0.25, 0.50) were measured systematically. The time-averaged velocity, root mean square velocity fluctuations, in-plane turbulent kinetic energy (TKE), instantaneous maximum velocity, and higher-order statistics including skewness and kurtosis were analysed. The results show that the vertical velocity fluctuation on the intermediate streamwise plane ($z/W$ = 0.25) reaches peak value approximately $v$rms/Ulid = 0.072 (where Ulid is the characteristic inflow velocity above the cavity), indicating the strongest local vertical unsteady response among the measured streamwise planes. On the near-wall plane ($z/W$ = 0.10), the instantaneous maximum velocity exhibits skewness 1.23 and kurtosis 7.47, reflecting pronounced local intermittent extreme events, whereas the extreme events on the middle plane ($z/W$ = 0.50) are weaker and closer to a Gaussian-like distribution. The spanwise planes also demonstrate clear non-Gaussian features, with high-fluctuation and high in-plane TKE regions primarily concentrated in areas associated with the interaction between the shear layer and recirculation structures, revealing significant spatial anisotropy in the flow. These results suggest that shear-layer development, primary recirculation, sidewall confinement and downstream flow turning jointly modulate local extreme events and intermittent velocity fluctuations. This study provides systematic experimental quantification of spatial non-uniformity and extreme-event characteristics in a high-Reynolds-number deep cavity using multi-plane PIV, offering useful experimental references for turbulence model validation and complex cavity-flow control.

The dynamics of non-spherical particles in wall-bounded flows plays a fundamental role in numerous natural and industrial processes, such as pollen dispersion, fibre suspensions and biomedical flows. Predictive simulations of such systems often employ an Euler–Lagrange approach, for which the accuracy hinges on the drag model used. Although reliable correlations exist for particles in unbounded flow or in direct contact with a wall, the intermediate regime of finite wall distance has received little attention, despite its prevalence in practical applications. For prolate spheroids – a common non-spherical particle shape – the drag force in this regime results from a complex interplay among the particle Reynolds number, the wall-normal distance and the particle’s three-dimensional orientation. In this work, we develop a drag model for prolate spheroids (aspect ratio $\lambda = 2$) that incorporates these coupled effects through a hierarchical framework based on three reference orientations and wall correction factors. The formulation recovers the exact unbounded Stokes behaviour and establishes near-wall asymptotic limits. Validation against direct numerical simulations across the parameter space considered yields a mean relative error below 2 %. Within its validated range, the proposed model accurately captures orientation-dependent wall effects for drag predictions, and is applicable in Euler–Lagrange simulations of non-spherical particles in wall-bounded flows.

Low-grade attapulgite clay intergrowths, composed mainly of attapulgite, illite, calcite and quartz, are abundant. The intimate intergrowth among these phases buries active surfaces under inert components, limiting performance and hindering value-added utilization. Herein, a multi-scale structural regulation strategy was employed. Sieving and water washing removed inert components for compositional regulation. High-speed shearing and ultrasonic treatment broke soft agglomerates and exposed active surfaces, achieving aggregation state regulation. Controlled thermal treatment removed channel water, activated exchangeable sites and enhanced negative surface charge, thereby regulating structural and surface chemistry. These steps progressively modified the material from its macroscopic composition down to its atomic-level surface properties. After the regulation, Pb2+ adsorption capacity increased from 153.1 to 237.5 mg g–1. The material remained effective even at pH 1 and reduced Pb2+ from 1000 to 0.83 μg L–1, well below the World Health Organization (WHO) guideline of 10 μg L–1. X-ray diffraction, X-ray photoelectron spectroscopy and quantitative cation release analysis were performed to elucidate the adsorption mechanism. Ion exchange dominated by Ca-rich mineral serves as the primary pathway. Cation exchange on silicate surfaces, forming PbSiO3, provides a key supplementary contribution. Electrostatic attraction and in situ precipitation participate synergistically. This work establishes a theoretical foundation and offers a reagent-free, industrially feasible route for the value-added utilization of low-grade intergrown clay resources in environmental remediation.

Translating academic discoveries into clinical investigation remains a major barrier in translational science, particularly when investigators lack the regulatory infrastructure required to sponsor clinical trials. These challenges are especially pronounced for natural product therapeutics, which often lack commercial sponsorship despite promising preclinical evidence. The Penn State Cancer Institute established an Investigator-Initiated Trial Sponsor Support Unit (SSU) to facilitate investigator-initiated trials and support progression toward National Cancer Institute designation. We describe a translational science case study of a Sponsor-Proxy operational model in which a centralized institutional unit performs sponsor-level regulatory and trial infrastructure functions while investigators retain scientific and clinical leadership. Two National Institutes of Health-funded trials evaluating Angelica gigas Nakai extract (INM176) illustrate this framework. The SSU authored regulatory documentation including Investigator’s Brochures, coordinated Investigational New Drug submissions, and supported trial infrastructure while clinical teams maintained participant oversight. Both trials achieved activation timelines of 112 and 105 days, shorter than previously reported activation times and within National Cancer Institute operational benchmarks. Sponsor-level oversight continued throughout trial conduct, including regulatory reporting, safety monitoring, and protocol amendment support. This case study demonstrates how centralized institutional expertise can overcome sponsor-level barriers and enable translation of basic science discoveries into investigator-initiated early-phase clinical trials.

A numerical study is conducted on a self-propelled flexible plate moving above an array of wall-mounted compliant blades at Reynolds numbers of order 100, designed to mimic flatfish-like locomotion near the vegetation-covered seabed. Compared with a flat rigid wall located at the same vertical distance from the plate, the blade-covered wall enhances the propulsive performance of the plate, yielding a higher propulsive velocity and lower energy consumption per transport distance. This enhancement intensifies as the blade spacing and vertical distance between the plate and the bottom wall decrease, but diminishes when the flapping frequency approaches the natural frequency of the blades due to resonance. By accounting for the asymmetric motion of the plate during the upper and lower half-periods, a universal scaling law ${\textit{Re}}_c\sim ({\textit{Re}}_{f,\delta })^{3/2}$ is identified between the propulsive Reynolds number ${\textit{Re}}_c$ and the corrected flapping Reynolds number ${\textit{Re}}_{f,\delta }$. Under the excitation of the flapping plate, two distinct vibration patterns are identified for the blade array. The first is the flapping-driven mode, which propagates synchronously with the plate’s motion. The second exhibits forward-propagating travelling waves, which are generated by the cooperative underdamped vibrations of downstream blades, and have a phase velocity much faster than the propulsive velocity of the plate. This study may shed light on the design of bio-inspired underwater vehicles and flow sensing techniques.

Direct numerical simulations are performed to elucidate the influence of counter- and co-rotation on turbulent viscoelastic Taylor–Couette flow in the Rossby number range $ \textit{Ro}^{-1}=-0.6$ to $ \textit{Ro}^{-1}=1$. A novel polymer-induced transition pathway is discovered that is fundamentally different from Newtonian flows. In the counter-rotation regime, the neutral surface is elastically modified and separates the turbulent inner-wall region containing chaotic vortices from the relaminarised layer adjacent to the outer cylinder. Strikingly, co-rotation triggers an elasto-rotational instability, which leads to the breakdown of large-scale Taylor vortices into small-scale penetrating structures, thereby preventing the relaminarisation at high co-rotation rates. Examination of turbulence dynamics demonstrates that the structural changes with increasing $ \textit{Ro}^{-1}$ are accompanied by a transition from elasto-inertial to elastically dominated turbulence. Specifically, the polymer stress progressively exceeds the Reynolds stress and monotonically enhances angular momentum transport, which eliminates the optimal transport characteristic found in the Newtonian flow. The elastically dominated nature of the turbulent flow under co-rotation is further corroborated by the more significant elastic production of the turbulent kinetic energy, as well as the monotonic enhancement of the polymer elongation as $ \textit{Ro}^{-1}$ increases. Furthermore, it is indicated that the polymer orientation strongly depends on vortical structures, with enhanced radial alignment occurring in the boundary region between adjacent vortices. This vortex-mediated polymer orientation is crucial for generating substantial polymer shear stress, establishing a direct link between coherent structures and polymer dynamics.

Debt financing plays a critical role in the financial sustainability of nonprofit organizations, yet scholarly understanding of the factors influencing nonprofits’ access to debt remains limited. This study explores the antecedents of creditworthiness among Chinese nonprofits, focusing on the role of political connections, revenue diversification, and organizational size. Drawing on signaling theory, we argue that these factors serve as credible signals to lenders, reducing information asymmetry and thereby enhancing nonprofits’ access to debt. Using survey data from Chinese nonprofits, we find that political connections and organizational size significantly improve creditworthiness, while revenue diversification does not. The findings also reveal a heavy reliance on debt among Chinese nonprofits, highlighting the importance of debt financing as a useful financial tool in resource-constrained environments. This study adds to the literature on nonprofit debt by presenting evidence from the unique and underexplored authoritarian context of China and by offering practical insights for nonprofits seeking to enhance their creditworthiness.

This meta-analysis synthesized 88 studies to investigate the processing advantages of emotion words over neutral words. Additionally, we explored the moderating effects of emotional properties (valence, arousal, emotion word type), linguistic factors (concreteness, frequency, length, neighborhood size), and task type in L1, advanced L2, and intermediate L2 speakers. We found a significant valence effect, with positive words showing a greater processing advantage than negative words only in L1 speakers. For arousal, the interaction analysis revealed that high arousal reduced the processing advantage of emotion words to a greater extent in intermediate L2 speakers than in L1 speakers. Furthermore, only advanced L2 speakers showed a significant processing advantage for emotion-label words compared to emotion-laden words. Regarding linguistic factors, longer word length was associated with greater processing advantages compared to shorter word length, but only in advanced L2 speakers. The greater processing advantage for concrete over abstract emotion words was observed only in intermediate L2 speakers, indicating that this group was the most sensitive to concreteness among all language speaker groups. Finally, task type significantly influenced emotion word processing in interaction with language proficiency. Overall, our findings support theoretical frameworks in both L1 and L2 processing and cognition.

Accurate prediction of the hydrodynamic coefficients of non-spherical particles in wall-confined flows is crucial for understanding particle–fluid interactions and reliable modelling of particle motion. Under strong wall confinement, the hydrodynamic coefficients exhibit a highly nonlinear dependence on the Reynolds number, wall distance and particle orientation – posing significant modelling challenges. In this study, we propose a multi-stage physics-informed machine-learning (MSPIML) framework for modelling the drag, lift and pitching torque coefficients of a wall-bounded prolate spheroid over the explored parameter space. In the first stage, a physics-informed mixture-of-experts (PIMoE) model predicts the drag coefficient by intelligently blending empirical correlations with a data-driven statistical expert. The resulting high-fidelity drag coefficient is then injected as an auxiliary input to a second-stage model, either a deep neural network (DNN) or an additional MoE, that predicts lift and pitching torque coefficients, thereby leveraging the strong physical coupling among the three coefficients. Trained on a comprehensive dataset of 720 direct numerical simulations covering wide ranges of Reynolds number, wall distance and particle orientation, the optimal PIMoE–DNN and PIMoE–MoE configurations achieve relative errors below 2.2 % for drag, 11.4 % for lift and 7.0 % for pitching torque while maintaining excellent generalisation across the entire parameter space. Moreover, the Shapley additive explanations analysis confirms that the MSPIML framework correctly captures the physical dependencies: dominant influence of Reynolds number and strong pitching torque dependence on the drag coefficient. The MSPIML framework provides an interpretable and efficient approach to the prediction of hydrodynamic coefficients and offers substantial potential for dynamic modelling of non-spherical particles in multiphase flows.

Systematic reviews (SRs) are critical for evidence-based research but are time-consuming and labor-intensive. The rapid expansion of academic publications further challenges the performance and applicability of existing screening and classification methods. While large language models (LLMs) present new opportunities for automation, limited research has examined whether they can achieve classification performance comparable to human reviewers in large-scale, multi-class settings. With the goal of improving classification performance, we proposed an LLM-based framework that leverages full-text key-insight extraction to enhance literature classification. We constructed a manually curated dataset of 900 articles from 17 published SRs to quantitatively evaluate the classification capabilities of LLMs. The results provided empirical evidence of LLMs’ potential in supporting large-scale SRs and introduced a practical pathway for improving efficiency and reliability in evidence synthesis. Empirical results showed that key-insight-based classification (KBC) significantly outperforms abstract-based classification (ABC). We implemented a confidence-weighted voting (CWV) mechanism using multiple LLMs to improve robustness. The CWV method achieved the highest macro F1-score of 0.796, substantially exceeding KBC (0.732), ABC (0.676), and unsupervised K-means clustering (0.446). By employing zero-shot LLMs, our approach demonstrated the potential for enhanced adaptability across diverse domains and classification tasks without requiring fine-tuning, demonstrating that a carefully designed pipeline can enable LLMs to achieve classification performance comparable to human reviewers.

The rise of internet celebrity cities has become one of the most striking phenomena in China since 2021. How do local governments respond and harness this trend to advance their development goals? This study focuses on local experimentation in creating such cities, drawing on the case of the Village Football Super League (Cun chao 村超) in Guizhou. We identify policy entrepreneurship as a key driver of local experimentation and highlight three core strategies for creating internet celebrity cities: crafting local symbols, co-producing viral content and mitigating public opinion risk. Further analysis shows that this experimentation, by attracting massive public attention, simultaneously promotes economic growth, strengthens social cohesion, reinforces state narratives and projects China’s national image onto the global stage. Overall, the findings suggest an emerging model in which public attention becomes a core resource for local development and governance in China’s digital era.

Change point analysis (CPA) detects structural shifts in a response sequence by partitioning it into segments with different statistical properties. This paper proposes three CPA approaches based on the Schwarz information criterion (SIC; hereafter SIC-CPA): response data only, response time (RT) data only, and the combination of response and RT data, to detect the prevalent test speededness in time-limit tests. To comprehensively investigate the efficiency and accuracy of the proposed approaches, six simulation studies were conducted under diverse conditions. Simulation results demonstrate that SIC-CPA can effectively enhance the power of change point detection and reduce Type I errors, while improving computational efficiency compared to the likelihood ratio and Wald tests. Moreover, the SIC-CPA combining response and RT data outperforms the SIC-CPA based solely on RTs, and the latter is substantially superior to the SIC-CPA based solely on responses. In addition, SIC-CPA accurately identifies two change points in RT patterns, corresponding to early warm-up and later test speededness. Using an iterative detect–clean–recalibrate procedure, SIC-CPA achieves more reliable Type I error control than likelihood ratio and Wald tests when item parameters are estimated from contaminated data. A real data analysis was conducted to show the application of the proposed approaches.

Dietary plant-derived bioactive compounds for enhancing physiological health are becoming a prevalent strategy for antibiotic alternatives. Our study revealed the effects and underlying mechanism of osthole (OST) and OST-tetramethylpyrazine (TMP) compound in Litopenaeus vannamei based on network pharmacology, molecular docking and a 42-d feeding trial verification. The results illustrated that OST and OST-TMP compound significantly improved the survival rate, weight gain rate, specific growth rate and feed conversion ratio, strengthening the growth performance of L. vannamei. Meanwhile, combining the predictive results from network pharmacology and molecular docking, we propose that OST and TMP synergistically enhance the antioxidant defence capacity of shrimp through the synergistic Nrf2 signalling pathway, thereby enhancing the expression of total antioxidant capacity, superoxide dismutase, catalase and glutathione peroxidase. Furthermore, OST-TMP exhibited a significant increase of the immune response in haemocyte and intestine of shrimp, increasing the expression of antimicrobial peptide and lysozyme and suppressing the inflammatory factors, via the synergistic (NF-κB) and complementary targets predicted by network pharmacology. Additionally, gut microbiota composition of L. vannamei was improved, and the dominant genera were correlated with intestinal immune in the compound groups. For the first time, we elucidated the mechanism of plant-derived bioactive compounds mediating physiological health in aquatic animals via a new strategy of network pharmacology-molecular docking-experimental verification and identified the optimal addition amount of OST-TMP in shrimp (150 mg/kg TMP + 20 mg/kg OST), providing a technical safeguard for the animal health and the safety of aquatic products.

The study of rotating Rayleigh–Taylor (RT) turbulence is of fundamental significance for geophysical processes and certain engineering applications. This work systematically investigates the effects of rotation on RT turbulence using direct numerical simulation (DNS), focusing primarily on the generation of kinetic energy and enstrophy, as well as the scale-to-scale transfer of kinetic energy. Based on the DNS results, it is demonstrated that there is a notable delay and inhibition of the mixing layer growth with enhancing rotation (quantified as a decreasing Rossby number, $Ro$). That is, energy conversion efficiency drops substantially, from approximately $50\,\%$ in the non-rotating case $Ro = \infty$ to only $10\,\%$ in the strong rotating case $Ro=0.1$. This is because rotation amplifies the viscous dissipation associated with the shear stress components in the vertical direction within the mixing layer. Regarding enstrophy generation, baroclinic effects dominate during the early stage of flow evolution, while vortex stretching and tilting become the primary contributors in the later stage. Notably, the vortex stretching and tilting term is significantly suppressed by the rotation, resulting in three-dimensional RT turbulence exhibiting an enstrophy generation mechanism more akin to two-dimensional flow. Furthermore, analysis of scale-to-scale transfer of kinetic energy reveals an increased likelihood of local inverse energy transfer events under enhanced rotation. Specifically, strong rotation (e.g. $Ro=0.1$) results in strongly helical turbulence, which contains more high-helicity regions favourable for local inverse energy transfer. Moreover, the presence of rotation leads to more coherent and elongated flow structures and an enhanced efficiency of fluid mixing within the mixing layer.

To mitigate inhomogeneous thermal and stress effects caused by multi-pulse accumulation in laser–material interactions, we propose an all-optical strategy to generate a structured beam, termed a ‘drill-like laser’, featuring a petal-shaped intensity profile with stochastic rotation by shot-to-shot control. The strategy involves generating collinear signal and idler pulses carrying conjugated orbital angular momenta via optical parametric amplification (OPA). The pulses then interfere with each other to form a beam with petal-shaped intensity structure, whose orientation is governed by their carrier-envelope phase (CEP) difference. The strategy is further implemented with a dual-stage OPA system pumped by an 800-Hz–30-fs–800-nm femtosecond laser, where the CEP difference is directly controlled by the pump CEP. Experimentally, a drill-like laser at 1.6 μm is demonstrated with stochastic shot-to-shot intensity rotation, resulting from the shot-to-shot random fluctuation of the pump CEPs, which has been validated using dual-line pump–probe detection via sum-frequency generation. Crucially, since the interference arises from two beams with free-space eigenmodes, rather than angular-dispersion-based spatiotemporal coupling, the drill-like laser maintains high propagation stability and is scalable in power by conventional laser amplification, holding great potential for applications in precision laser processing and other high-field scenarios.