Viscous fingering, a classic hydrodynamic instability, is governed by the the competition between destabilising viscosity ratios and stabilising surface tension or thermal diffusion. We show that the channel confinement can induce ‘diffusion’-like stabilising effects on viscous fingering even in the absence of interfacial tension and thermal diffusion, when a clear oil invades the mixture of the same oil and non-colloidal particles. The key lies in the generation of long-range dipolar disturbance flows by highly confined particles that form a monolayer inside a Hele-Shaw cell. We develop a coarse-grained model whose results correctly predict universal fingering dynamics that is independent of particle concentrations. This new mechanism offers insights into manipulating and harnessing collective motion in non-equilibrium systems.

The selection of random sampling points is crucial for the path quality generated by probabilistic roadmap (PRM) algorithm. Increasing the number of sampling points can enhance path quality. However, it may also lead to extended convergence time and reduced computational efficiency. Therefore, an improved probabilistic roadmap algorithm (TL-PRM) is proposed based on topological discrimination and lazy collision. TL-PRM algorithm first generates a circular grid area among start and goal points. Then, it constructs topological nodes. Subsequently, elliptical sampling areas are created between each pair of adjacent topological nodes. Random sampling points are generated within these areas. These sampling points are interconnected using a layer connection strategy. An initial path is generated using a delayed collision strategy. The path is then adjusted by modifying the nodes on the convex outer edges to avoid obstacles. Finally, a reconnection strategy is employed to optimize the path. This reduces the number of path waypoints. In dynamic environments, TL-PRM algorithm employs pose adjustment strategies for semi-static and dynamic obstacles. It can use either the same or opposite pose adjustments to avoid dynamic obstacles. Experimental results indicate that TL-PRM algorithm reduces the average number of generated sampling points by 70.9% and average computation time by 62.1% compared with PRM* and PRM-Astar algorithms. In winding and narrow passage maps, TL-PRM algorithm significantly decreases the number of sampling points and shortens convergence time. In dynamic environments, the algorithm can adjust its pose orientation in real time. This allows it to safely reach the goal point. TL-PRM algorithm provides an effective solution for reducing the generation of sampling points in PRM algorithm.

Porcine small intestinal epithelial cell line (IPEC-J2) is a good research model exploring the impact of feed additives on intestinal epithelial cells. Monobutyrin (MB), as a derivative of butyric acid (BA), overcomes the shortcomings of BA. MB can maintain intestinal barrier function in animals, but its underlying regulatory mechanism is unknown. Thus, we used IPEC-J2 cells as the research object. We were using real-time fluorescence quantitative PCR, western blot, immunofluorescence, and transcriptomics technology to explore the effect of MB on the barrier function of IPEC-J2 cells and its regulatory mechanism. The results found that MB treatment could cause IPEC-J2 cells to occur a response to hypoxia at the transcriptional level, thereby increasing the expression of hypoxia-inducible factor 1 and phospho-extracellular signal-regulated kinase 1/2 protein and improving the expression of tight junction proteins. Therefore, MB can alleviate the activation of the NF-κB signaling pathway. In addition, MB mitigates the damage to cell transmembrane glycoproteins, microvilli, and tight junctions caused by lipopolysaccharides (LPS) stimulation, thus resisting the effects of LPS. As a dietary supplement, MB has good application prospects in maintaining the intestinal epithelial barrier function of animals.

Fuel pre-injection in the inlet of a hypersonic engine has been proven to be advantageous in the range of the very high flight Mach numbers. In this paper, a rapid inlet performance analysis model with fuel pre-injection is proposed. The modelling process is divided into two stages. Firstly, the baseline inlet model is provided based on the working principle of the inlet. Then, the newly proposed fuel injection and heat release model is added to the baseline inlet model. Among them, the fuel injection and heat release model is equivalent to increasing the compression angle in the cold state. And in the hot state the effect of the fuel heat release will be considered in addition to the effect of cold state. The research results show as the equivalence ratio increases, the equivalent compression angle also increases, but the two are not in a linear relationship. Based on this pattern of effect, fuel injection can be used to regulate the shock wave position and accurately control the flow rate of the inlet. In addition, by comparing to numerical simulation, it is found that the analysis model can almost reasonably predict the performance of the pre-injection inlet. However, the calculation of drag coefficient has some deviation compared to numerical simulation, which is probably due to the lack of consideration of friction drag and the interaction between the shock wave and boundary layer in the model analysis. Overall, the modelling method proposed in this paper can reflect the effect of fuel injection on inlet performance, which can be used to optimise injection strategy in the future.

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).

The dependence of the Richtmyer–Meshkov instability (RMI) on post-shock Atwood number ($A_1$) is experimentally investigated for a heavy–light single-mode interface. We create initial interfaces with density ratios of heavy to light gases ranging from 1.73 to 34.07, and achieve the highest $|A_1|$ value reported to date for gaseous-interface experiments (0.95). For the first time, spike acceleration is observed in experiments with a heavy–light configuration. The models for the start-up, linear and weakly nonlinear evolution stages are evaluated over a wide range of $A_1$ conditions. Specifically, the models proposed by Li et al. (Phys. Fluids, vol. 36, 2024, 056104) and Wouchuk & Nishihara (Phys. Plasmas, vol. 4, 1997, 1028–1038) effectively describe the start-up and linear stages, respectively, across all cases. None of the considered nonlinear models is valid under all $A_1$ conditions. Based on the dependence of spike and bubble evolutions on $A_1$ provided by the present work and previous study (Chen et al., J. Fluid Mech., vol. 975, 2023, A29), the SEA model (Sadot et al., Phys. Rev. Lett., vol. 80, 1998, pp. 1654–1657), whose expression has clear physical meanings, is modified by revising the coefficient that governs its prediction for early-time evolution. The modified model applies to prediction of the weakly nonlinear evolution of RMI with $A_1$ ranging from −0.95 to −0.35 and from 0.30 to 0.86. Based on this model, an approximation of the critical $A_1$ for the occurrence of spike acceleration is obtained.

Bronze mou vessels appear in Shu tombs in south-west China during the Eastern Zhou period (c. 771–256 BC). Examination of these vessels reveals major changes in the supply of metal and alloying technology in the Shu State, throwing new light on the social impact of the Qin conquest and later unification of China.

The manipulation of the Richtmyer–Meshkov instability growth at a heavy–light interface via successive shocks is theoretically analysed and experimentally realized in a specific shock-tube facility. An analytical model is developed to forecast the interface evolution before and after the second shock impact, and the possibilities for the amplitude evolution pattern are systematically discussed. Based on the model, the parameter conditions for each scenario are designed, and all possibilities are experimentally realized by altering the time interval between two shock impacts. These findings may enhance the understanding of how successive shocks influence hydrodynamic instabilities in practical applications.

In this paper, we propose a joint modeling approach to analyze dependency in parallel response data. We define two types of dependency: higher-level dependency and within-item conditional dependency. While higher-level dependency can be estimated with common latent variable modeling approaches, within-item conditional dependency is a unique kind of information that is often not captured with extant methods, despite its potential to shed new insights into the relationship between the two types of response data. We differentiate three ways of modeling within-item conditional dependency by conditioning on raw values, expected values, or residual values of the response data, which have different implications in terms of response processes. The proposed approach is illustrated with the example of analyzing parallel data on response accuracy and brain activations from a Theory of Mind assessment. The consequence of ignoring within-item conditional dependency is investigated with empirical and simulation studies in comparison to conventional dependency analysis that focuses exclusively on relationships between latent variables.

An asymptotic matching modal model is established based on the singular perturbation method for predicting mode evolution in single- and dual-mode interfaces accelerated by a shock wave. The startup process is incorporated into the model to provide a complete description of the mode evolution after the shock impact. Through considering the feedback from high-order harmonic to the third-order harmonic, the model accuracy is improved and the model divergence is prevented. In addition, the model can evaluate the mutual-coupling effect on the amplitude variations of high-order harmonics besides the ‘beat modes’. To validate the model, experiments on both light–heavy and heavy–light interfaces subject to a shock wave are conducted, and both single- and dual-mode interfaces formed by the soap-film technique are involved. The interface profiles extracted from mode decomposition and predicted by the model show high consistency with the experimental counterparts. Good agreement of the mode amplitude growths between the experiments and theoretical predictions shows the superiority of the model, especially for the heavy–light interface.

This study aims to investigate the effects of the vine of Lonicera japonica Thunb (VLT) and marine-derived Bacillus amyloliquefaciens-9 (BA-9) supplementation on the growth performance, antioxidant capacity, and gut microbiota of goat kids. A total of 32 4-week-old kids were randomly assigned into four groups: a control group (CON), a group supplemented with 0.3% BA-9 (BA-9), a group supplemented with 2% VLT (VLT), and a group supplemented with both 0.3% BA-9 and 2% VLT (MIX). The results indicated that VLT supplementation significantly increased both average daily (P < 0.001) and total weight gain (TWG) (P < 0.001), while BA-9 alone had no significant effect (P > 0.05) on the average daily and TWG. Biomarker analysis of oxidative stress revealed that supplementation of VLT or BA-9 alone enhanced antioxidant capacity. The MIX group showing a higher total antioxidant capacity (T-AOC) compared with the CON, VLT, and BA-9 groups (P < 0.05). Plasma albumin (ALB) levels were significantly increased in the both VLT and BA-9 groups. Microbiota analysis revealed significant differences in α-diversity and β-diversity between the MIX and CON groups, with specific genera such as Prevotellaceae_UCG.004 and Rikenellaceae_RC9_gut_group negatively correlated with average daily gain (ADG), while Alistipes was positively correlated with T-AOC. These findings suggest that the combined supplementation of VLT and BA-9 can significantly enhance growth performance and antioxidant capacity in goat kids by modulating the composition of gut microbiota and reducing oxidative stress.

We study the existence and strong instability of standing wave solutions for the fractional nonlinear Schrödinger equation

\begin{equation*}\begin{cases}\mathrm{i} \psi_{t}=\left(-\Delta\right)^s \psi-\left(|\psi|^{p-2}\psi+\mu|\psi|^{q-2}\psi\right), & \quad(t,x)\in(0,\infty)\times\mathbb{R}^N,\\\psi(0,x)=\psi_0(x),&\quad x\in\mathbb{R}^N,\end{cases}\end{equation*}

where $N\geq2$, $0 \lt s \lt 1$, $2 \lt q \lt p \lt 2_s^*=2N/(N-2s)$, and $\mu\in\mathbb{R}$. The primary challenge lies in the inhomogeneity of the nonlinearity.We deal with the following three cases: (i) for $2 \lt q \lt p \lt 2+4s/N$ and µ < 0, there exists a threshold mass a0 for the existence of the least energy normalized solution; (ii) for $2+4s/N \lt q \lt p \lt 2_s^*$ and µ > 0, we reveal the existence of the ground state solution, explore the strong instability of standing waves, and provide a blow-up criterion; (iii) for $2 \lt q\leq2+4s/N \lt p \lt 2_s^*$ and µ < 0, the strong instability of standing wave solutions is demonstrated. These findings are illuminated through variational characterizations, the profile decomposition, and the virial estimate.

The proton–boron ${}^{11}{\text{B}}\left( {p,\alpha } \right)2\alpha $ reaction (p-11B) is an interesting alternative to the D-T reaction ${\text{D}}\left( {{\text{T}},{\text{n}}} \right)\alpha $ for fusion energy, since the primary reaction channel is aneutronic and all reaction partners are stable isotopes. We measured the α production yield using protons in the 120–260 keV energy range impinging onto a hydrogen–boron-mixed target, and for the first time present experimental evidence of an increase of α-particle yield relative to a pure boron target. The measured enhancement factor is approximately 30%. The experiment results indicate a higher reactivity, and that may lower the condition for p-11B fusion ignition.