Ionic surfactants are commonly employed to modify the rheological properties of fluids, particularly in terms of surface viscoelasticity. Concurrently, external electric fields can significantly impact the dynamics of liquid threads. A key question is how ionic surfactants affect the dynamic behaviour of threads in the presence of an electric field? To investigate this, a one-dimensional model of a liquid thread coated with surfactants within a radial electric field is established, employing the long-wave approximation. We systematically investigate the effects of dimensionless parameters associated with the surfactants, including surfactant concentration, dilatational Boussinesq number ${\textit{Bo}}_{\kappa \infty }$ and shear Boussinesq number ${\textit{Bo}}_{\mu \infty }$. The results indicate that increasing the surfactant concentration and the two Boussinesq numbers reduces both the maximum growth rate and the dominant wavenumber. In addition, both the electric field and surfactants mitigate the breakup of the liquid thread and the formation of satellite droplets. At low applied electric potentials, the surface viscosity induced by surfactants predominantly governs this suppression. Surface viscosity suppresses the formation of satellite droplets by maintaining the neck point at the centre of the liquid thread within a single disturbance wavelength. When the applied potential is high, the electric stress has two main effects: the external electric field exerts a normal pressure on the liquid thread surface, suppressing satellite droplet formation, while the internal electric field inhibits liquid drainage. Surface viscosity further stabilizes the system by suppressing flow dynamics during this process.

In aerospace, automated assembly line, and precision engineering, asymmetric multi-robot systems comprising serial and parallel robots leverage the complementary strengths of these configurations to address the conflicting demands of high load capacity, extensive range, and flexibility in assembly tasks. However, the relatively small workspace of the parallel robot limits the full potential of the collaborative system functionality. This paper centers on a collaborative assembly system involving serial-parallel robots, whose collaborative workspace is determined by using a combination of the Monte Carlo method and lattice method. Additionally, a multi-objective optimization model is developed to holistically evaluate the collaborative workspace performance. The optimization problem is solved by an enhanced NSGA-II algorithm, which yields a Pareto optimal solution set. This result offers valuable technical insights for designing collaborative systems tailored to diverse task requirements.

Wall pressure fluctuations (WPFs) over aerodynamic surfaces contribute to the physical origin of noise generation and vibrational loading. Understanding the generation mechanism of WPFs, especially those exhibiting extremely high amplitudes, is important for advancing design and control in practical applications. In this work, we systematically investigate extreme events of WPFs in turbulent boundary layers and the compressibility effects thereon. The compressibility effects, encompassing extrinsic and intrinsic ones, ranging from weak to strong, are achieved by varying Mach numbers and wall temperatures. A series of datasets at moderate Reynolds numbers obtained from direct numerical simulation are analysed. It is found that the intermittency of WPFs depends weakly on extrinsic compressibility effects, whereas intrinsic compressibility effects significantly enhance intermittency at small scales. Coherent structures related to extreme events are identified using volumetric conditional average. Under extrinsic compressibility effects, extreme events are associated with the weak dilatation structures induced by interactions of high- and low-speed motions. When intrinsic compressibility effects dominate, these events are associated with the strong alternating positive and negative dilatation structures embedded in low-speed streaks. Furthermore, Poisson-equation-based pressure decomposition is performed to partition pressure fluctuations into components governed by distinct physical mechanisms. By analysing the proportion of each pressure component in extreme events, it is found that the contributions of the slow pressure and viscous pressure exhibit weak dependence on the compressibility effects, especially the extrinsic ones, and the varying trend of contributions of the rapid pressure with compressibility effects is opposite to that of the compressible pressure component.

This study reports potassium (K) isotope compositions of diamondiferous kimberlites. Altered kimberlite samples exhibit δ41K values ranging from −1.293 ± 0.052 (2SD) to −0.114 ± 0.029 ‰, showing covariations with chemical indicators of alteration. This is consistent with the geochemical dynamics of K isotopes in hydrothermal fluid-related processes. In contrast, pristine kimberlite samples display restricted K isotope compositions, with δ41K values between −0.494 ± 0.057 and −0.270 ± 0.048 ‰. Notably, the δ41K values of these pristine kimberlite samples correlate well with K2O and Rb contents, suggesting that approximately ∼0.2 ‰ of K isotope fractionation is induced by phlogopite crystallization, as indicated by quantitative modelling. The estimated δ41K values of −0.458 ‰ for the primary kimberlite melt and of −0.414 ‰ for the kimberlite source imply a potential link to the bulk silicate Earth. These new measurements, along with literature data from various rocks, indicate that the K isotope composition in the deep mantle (>150 km) is more homogenous than in shallow regions, likely reflecting the efficiency of convection flow and K behaviour during subduction. In addition, the K isotope data reveal temporal variations in mantle-derived magmas from the Palaeozoic to the Cenozoic, highlighting the geological history and lithospheric destruction of the North China Craton. This study underscores the significance of K isotopes in enhancing our understanding of mantle dynamics, crustal recycling and the geochemical evolution of the Earth’s interior.

The limited stop-loss transform, along with the stop-loss and limited loss transforms – which are special or limiting cases of the limited stop-loss transform – is one of the most important transforms used in insurance, and it also appears extensively in many other fields including finance, economics, and operations research. When the distribution of the underlying loss is uncertain, the worst-case risk measure for the limited stop-loss transform plays a key role in many quantitative risk management problems in insurance and finance. In this paper, we derive expressions for the worst-case distortion risk measure of the limited stop-loss transform, as well as for the stop-loss and limited loss transforms, when the distribution of the underlying loss is uncertain and lies in a general $k$-order Wasserstein ball that contains a reference distribution. We also identify the worst-case distributions under which the worst-case distortion risk measures are attained. Additionally, our results also recover the findings of Guan et al. ((2023) North American Actuarial Journal, 28(3), 611–625), regarding the worst-case stop-loss premium over a $k$-order Wasserstein ball. Furthermore, we use numerical examples to illustrate the worst-case distributions and the worst-case risk measures derived in this paper. We also examine the effects of the reference distribution, the radius of the Wasserstein ball, and the retention levels of limited stop-loss reinsurance on the premium for this type of reinsurance.

Based on the characteristics of the variable pivot gait during the human load-carrying, this paper proposes a double-leg coordination assistance principle for load-carrying: assisting support of the guiding leg at the heel-pivot stage by the spring to reduce the collision, which can reduce the ankle moment of the following leg that is performing the push-off at the toe-pivot stage. A novel unpowered load-carrying exoskeleton (ULE) with a double-support closed-chain configuration is designed, and the theoretical verification is carried out. Five subjects participate in the load-carrying and metabolic cost experiments for assisting and energy-saving effect evaluation, and the angle and moment of human joints, plantar pressure, spring compression and human net metabolic rate are analyzed. Compared with carrying load by the human alone, wearing the novel ULE with spring reduces the human peak ankle moment performing the push-off by up to 11.9 ± 1.6% (Mean±SE, 10 kg), average ankle moment over the support phase by up to 36.8 ± 9.1% (Mean±SE, 5 kg) and the average vertical plantar pressure by up to 8.1 ± 1%% (Mean±SE, 15 kg). Meanwhile, wearing the novel ULE reduces the human net metabolic rate by 5.6 ± 0.5% (Mean±SE, 10 kg), 4.1 ± 0.7% (Mean±SE, 15 kg) and 5.9 ± 1.6% (Mean±SE, 20 kg). The results show that the novel ULE can provide support and joint moment assistance over the whole support phase while reducing human net metabolic rate. This study can also be applied to the powered load-carrying exoskeleton, providing a new avenue.

This study employs a direct numerical simulation method to investigate the wake pattern evolutions of flows past an insulated spheroid and provides expressions of force and torque coefficients influenced by a streamwise magnetic field in an incompressible, conducting, viscous fluid. A total of 1150 cases are examined covering a parameter range of Reynolds number $50 \leqslant \textit{Re} \leqslant 250$, aspect ratio $1.5 \leqslant \beta \leqslant 6$, inclination angle $0^\circ \leqslant \theta \leqslant 90^\circ$, and interaction parameter $0 \leqslant N \leqslant 10$, where $\beta$ and $N$, respectively, reflect the anisotropy of the spheroid and the strength of magnetic field. Nine wake patterns are classified based on wake structure features and summarised in three maps of regimes according to the inclination angle. The transition mechanisms among these wake patterns are also investigated under the influence of a streamwise magnetic field. Furthermore, expressions for drag, lift and torque coefficients are derived with the help of three fundamental physical criteria. Results indicate that the force and torque expressions give a good prediction within the present parameter space $\{\textit{Re}, \beta , \theta , N\}$.

Shock interactions on a V-shaped blunt leading edge (VBLE) that are commonly encountered at the cowl lip of an inward-turning inlet are investigated at freestream Mach numbers ($ M_\infty$) 3–6. The swept blunt leading edges of the VBLE generate a pair of detached shocks with varying shapes due to the changes in $ M_\infty$ and $L/r$ (i.e. the ratio of the leading-edge length $L$ to the leading-edge blunt radius $r$), which causes intriguing shock interactions at the crotch of the VBLE. Three subtypes of regular reflection (RR) and a Mach reflection (MR) are produced successively with increasing $ M_\infty$ for a given $L/r$, which appear in the opposite order to those with increasing $L/r$ for a given $ M_\infty$. These shock interactions identified in numerical simulations are verified in supersonic and hypersonic wind tunnel experiments. It is demonstrated that the relative position of the shocks is crucial in determining the transitions of shock interactions by varying either $L/r$ or $ M_\infty$. Transition criteria between subtypes of RR and from RR to MR are theoretically established in the parameter space $(M_\infty,L/r)$ by analysing the shock structures, showing good agreement with the numerical and experimental results. Interactions between either immature or fully developed detached shocks are embedded in these criteria. Specifically, the transition criteria asymptotically approach the corresponding critical $ M_\infty$ when $L/r$ is sufficiently large. These transition criteria provide guidelines for improving the design of the cowl lip of an inward-turning inlet in supersonic and hypersonic regimes.

The high comorbidity of major depressive disorder (MDD), anxiety disorders (ANX), and post-traumatic stress disorder (PTSD) complicates the study of their structural neural correlates, particularly in white matter (WM) alterations. Using fractional anisotropy (FA), this meta-analysis aimed to identify both unique and shared WM characteristics for these disorders by comparing them with healthy controls (HC). The aggregated sample size across studies includes 3,661 individuals diagnosed with MDD, ANX, or PTSD and 3,140 HC participants. The whole-brain analysis revealed significant FA reductions in the corpus callosum (CC) across MDD, ANX, and PTSD, suggesting a common neurostructural alteration underlying these disorders. Further pairwise comparisons highlighted disorder-specific differences: MDD patients showed reduced FA in the middle cerebellar peduncles and bilateral superior longitudinal fasciculus II relative to ANX patients and decreased FA in the CC extending to the left anterior thalamic projections (ATPs) when compared with PTSD. In contrast, PTSD patients exhibited reduced FA in the right ATPs compared to HC. No significant FA differences were observed between ANX and PTSD or between ANX and HC. These findings provide evidence for both shared and unique WM alterations in MDD, ANX, and PTSD, reflecting the neural underpinnings of the clinical characteristics that distinguish these disorders.

Coherent beam combining (CBC) of laser arrays is increasingly attracting attention for generating free-space structured light, unlocking greater potential in aspects such as power scaling, editing flexibility and high-quality light field creation. However, achieving stable phase locking in a CBC system with massive laser channels still remains a great challenge, especially in the presence of heavy phase noise. Here, we propose an efficient phase-locking method for a laser array with more than 1000 channels by leveraging a deep convolutional neural network for the first time. The key insight is that, by elegantly designing the generation strategy of training samples, the learning burden can be dramatically relieved from the structured data, which enables accurate prediction of the phase distribution. We demonstrate our method in a simulated tiled aperture CBC system with dynamic phase noise and extend it to simultaneously generate orbital angular momentum (OAM) beams with a substantial number of OAM modes.

This paper introduces a novel ray-tracing methodology for various gradient-index materials, particularly plasmas. The proposed approach utilizes adaptive-step Runge–Kutta integration to compute ray trajectories while incorporating an innovative rasterization step for ray energy deposition. By removing the requirement for rays to terminate at cell interfaces – a limitation inherent in earlier cell-confined approaches – the numerical formulation of ray motion becomes independent of specific domain geometries. This facilitates a unified and concise tracing method compatible with all commonly used curvilinear coordinate systems in laser–plasma simulations, which were previously unsupported or prohibitively complex under cell-confined frameworks. Numerical experiments demonstrate the algorithm’s stability and versatility in capturing diverse ray physics across reduced-dimensional planar, cylindrical and spherical coordinate systems. We anticipate that the rasterization-based approach will pave the way for the development of a generalized ray-tracing toolkit applicable to a broad range of fluid simulations and synthetic optical diagnostics.

The fall armyworm, Spodoptera frugiperda (J. E. Smith) (Lepidoptera: Noctuidae), is a highly destructive polyvorous pest with a wide host range and the ability to feed continuously with seasonal changes. This destructive pest significantly damages crops and can also utilize non-agricultural plants, such as weeds, as alternative hosts. However, the adaptation mechanisms of S. frugiperda when switching between crop and non-crop hosts remain poorly understood, posing challenges for effective monitoring and integrated pest management strategies. Therefore, this study aims to elucidate the adaptability of S. frugiperda to different host plants. Results showed that corn (Zea mays L.) was more suitable for the growth and development of S. frugiperda than wheat (Triticum aestivum L.) and goosegrass (Eleusine indica). Transcriptome analysis identified 699 genes differentially expressed when fed on corn, wheat, and goosegrass. The analysis indicated that the detoxification metabolic pathway may be related to host adaptability. We identified only one SfGSTs2 gene within the GST family and investigated its functional role across different developmental stages and tissues by analysing its spatial and temporal expression patterns. The SfGSTs2 gene expression in the midgut of larvae significantly decreased following RNA interference. Further, the dsRNA-fed larvae exhibited a decreased detoxification ability, higher mortality, and reduced larval weight. The findings highlight the crucial role of SfGSTs2 in host plant adaptation. Evaluating the feeding preferences of S. frugiperda is significant for controlling important agricultural pests.

We study the generalized Ramsey–Turán function $\mathrm {RT}(n,K_s,K_t,o(n))$, which is the maximum possible number of copies of $K_s$ in an n-vertex $K_t$-free graph with independence number $o(n)$. The case when $s=2$ was settled by Erdős, Sós, Bollobás, Hajnal, and Szemerédi in the 1980s. We combinatorially resolve the general case for all $s\ge 3$, showing that the (asymptotic) extremal graphs for this problem have simple (bounded) structures. In particular, it implies that the extremal structures follow a periodic pattern when t is much larger than s. Our results disprove a conjecture of Balogh, Liu, and Sharifzadeh and show that a relaxed version does hold.

High-fat food intake is associated with atopic dermatitis (AD), but the role of habitual dietary habits related to the frequency of high-fat food intake remains unclear. To address this, we developed a frequency-based dietary index, Diet Quality based on Dietary Fat Score, to assess high-fat food intake and examined its association with AD in 13 561 young Chinese adults (mean age = 22·51 years, (sd 5·90)) from Singapore and Malaysia. Using an investigator-administered questionnaire aligned with the validated International Study of Asthma and Allergies in Childhood protocol, we conducted multivariable logistic regression analysis, adjusting for demographics, body mass index, genetic predisposition and lifestyle factors, with false discovery rate correction for multiple comparisons. Frequent high-fat food intake was associated with higher odds of AD (adjusted OR (AOR): 1·53; 95 % CI: 1·31, 1·77; P< 0·001). The association remained significant regardless of total fat intake (AOR: 1·45; 95 % CI: 1·05, 1·80; P< 0·001) and among individuals with high fruit and vegetable intake (AOR: 1·49; 95 % CI: 1·19, 1·86; P< 0·001) or low energy intake (AOR: 1·40; 95 % CI: 1·05, 1·86; P< 0·05). No synergistic effects were observed between dietary factors. These findings highlight that frequent intake of high-fat foods is independently associated with AD, emphasising the potential of dietary moderation in AD risk management.

Spatial intensity modulation in amplified laser beams, particularly hot spots, critically constrains attainable pulse peak power due to the damage threshold limitations of four-grating compressors. This study demonstrates that the double-smoothing grating compressor (DSGC) configuration effectively suppresses modulation through directional beam smoothing. Our systematic investigation validated the double-smoothing effect through numerical simulations and experimental measurements, with comprehensive spatiotemporal analysis revealing excellent agreement between numerical and practical pulse characteristics. Crucially, the DSGC enables a 1.74 times energy output boost compared to conventional compressors. These findings establish the DSGC as a pivotal advancement for next-generation ultrahigh-power laser systems, providing a viable pathway toward hundreds of PW output through optimized spatial energy redistribution.

Among various deep learning-based SLAM systems, many exhibit low accuracy and inadequate generalization on non-training datasets. The deficiency in generalization ability can result in significant errors within SLAM systems during real-world applications, particularly in environments that diverge markedly from those represented in the training set. This paper presents a methodology to enhance the generalization capabilities of deep learning SLAM systems. It leverages their superior performance in feature extraction and introduces Exponential Moving Average (EMA) and Bayes online learning to improve generalization and localization accuracy in varied scenarios. Experimental validation, utilizing Absolute Trajectory Error (ATE) metrics on the dataset, has been conducted. The results demonstrate that this method effectively reduces errors by $20\%$ on the EuRoC dataset and by $35\%$ on the TUM dataset, respectively.