In extreme environments, hexapod robots are highly susceptible to locomotor failure due to leg structure damage or electronic circuit interruptions. Such failures are particularly critical in ipsilateral two-leg loss, where most conventional hexapod designs fail to maintain stability. This paper introduces a hexapod robot platform equipped with a functional arm (FA) and proposes a fault-tolerant strategy based on FA compensation. The FA serves as a task-oriented module under normal conditions and as a compensatory leg during instability caused by leg loss, enabling rapid recovery of stability and walking capacity without complex adjustments to the remaining legs. An optimization algorithm was developed for gait adjustment, using the longitudinal stability margin as the optimization objective with the compensation median angle θ and swing angle α as key parameters. The optimal compensatory positions of the FA were identified for both the ipsilateral middle-hind leg and front-hind leg failure scenarios. Experimental results demonstrate a marked improvement in both walking stability and capability after FA compensation. For both failure modes, the robot’s attitude fluctuations were significantly suppressed, and the stride length was substantially increased, affirming the feasibility and effectiveness of the proposed approach in enhancing the environmental adaptability of multi-legged robots.

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.

Test speededness, caused by time constraints, can impact examinees’ performance, leading to decreased response accuracy, particularly toward the end of the test. Most existing methods for detecting test speededness rely on specific distributional assumptions for response times (RTs), such as the lognormal distribution, which may lead to incorrect statistical inference if the true data distribution deviates from these assumptions. This article proposes a novel Bootstrap-CUSUM method for detecting test speededness, which is robust to non-normality in log-RTs. By constructing a cumulative sum (CUSUM) person-fit statistic for log-RTs and using the multiplier bootstrap to estimate its empirical distribution, our method facilitates individual-level detection and changepoint estimation. We prove the theoretical consistency of the method under both null and alternative hypotheses. Simulation studies show that the Bootstrap-CUSUM method outperforms the likelihood ratio test, Wald test, and score test in terms of correct classification rate, true detection rate, and false positive rate, demonstrating superior robustness and adaptability across different data distributions. The real data analysis further demonstrates the practical utility of the proposed method for detecting test speededness.

In the present study, we propose a novel skin-friction prediction formula based on a re-established self-similarity within the adverse-pressure-gradient (APG) turbulent boundary layer. The basic idea lies in introducing a novel velocity scale, which is derived mathematically and adapted physically from the linear total stress within the boundary layer. This scale assimilates concurrently and fundamentally the friction velocity, two distinct pressure-gradient velocity scales and the half-power law of the mean velocity in the intermediate region. Then, this scale formula is well validated across a comprehensive, multi-geometry database of APG flows over flat plates, curved plates, ramps and airfoils, which covers an unprecedented parameter range, with friction Reynolds number ranging from $10^2$ to $5\times 10^3$ and the Rotta–Clauser pressure-gradient parameter spanning from $10^{-1}$ to $10^2$. Crucially, the proposed scale consistently recovers a classical logarithmic region across all tested APG conditions, thereby restoring the self-similar structures traditionally absent in strong or non-equilibrium pressure-gradient flows. Leveraging this reconstructed self-similarity, we further formulate a new, robust skin-friction prediction model which demonstrates predictive errors confined within $\pm 20\,\%$ for all the investigated non-equilibrium flow states.

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.

Discrepancies in iodised salt coverage rate (ISCR) between household salt and that used in catering establishments may significantly compromise the accuracy of dietary iodine intake assessments. To evaluate this impact, we analysed data from the 2023 Shanghai Diet and Health Survey, a cross-sectional study involving 2920 adults. Dietary intake was assessed using three 24-h dietary recalls and an FFQ, while condiment intake was collected using the weighed inventory method. Additionally, salt samples from 960 canteens and restaurants were tested to determine the ISCR in dining establishments. Results showed that the ISCR was 85·9 % in dining establishments, markedly higher than the 53·3 % observed in households. Among employed participants in Shanghai, 51·7 %, 56·1 % and 18·7 % reported consuming breakfast, lunch and dinner outside the home at least once during the 3-d study period, respectively. The estimated daily iodine intake was 101 μg/d when dining-out salt was assumed to have the same ISCR as household salt, but it increased to 118 μg/d after accounting for the ISCR discrepancy. In conclusion, the rising prevalence of eating out has reshaped residents’ dietary habits, rendering traditional household-centric survey methods inadequate for iodine intake estimation in Shanghai. Incorporating ISCR differences between household and dining settings is essential for more accurate dietary iodine assessments.

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.

The hydrodynamic interactions involved in the self-organisation phenomenon in biological systems are not fully understood and have attracted significant attention. A previous study (Peng et al. 2018 J. Fluid Mech., vol. 853, pp. 587–600) found that, arranged in an unbounded fluid, the largest cluster of self-propelled bodies in tandem, capable of spontaneously forming an ordered configuration, consists of eight swimmers. Here, we numerically investigate the collective behaviour of multiple self-propelled plates in tandem within a channel of width $H$, confined by two parallel walls. These plates are driven by harmonic flapping motions of uniform frequency and amplitude. Results demonstrate for the first time that the channel confinement significantly enhances group cohesion, with up to 72 individuals self-organising into ordered configurations at an optimal channel width. We observe two stable configurations: a hybrid mode with subgroups (typically at smaller channel widths) and a regular mode with sparse configuration. In large regular-mode groups, the vortex fields downstream exhibit spatial periodicity, conforming to Rosenhead’s stability criterion for confined vortex streets (vortex spacing $L_{v\textit{or}} \geqslant 1.419H$). This theoretical alignment explains both the observed upper channel-width limit ($H \approx 4.0{-}4.5$) for large-scale cohesion and the robust order in the regular mode. The plates may adopt spontaneously a ‘vortex-slalom’ path, reducing the drag force and energy consumption while maximising stability. Deviation from this path results in a spring-like restoring force, promptly returning the plate to equilibrium.

We present an experimental study on electron and X-ray generation from the interaction of a hundreds of TW femtosecond laser with microchannels. Leveraging the guiding effect of the channel structure on both the laser and electrons, a well-collimated electron beam is achieved, with a beam charge of 1.5 nC (>10 MeV), a slope temperature of 9.1 MeV and a nearly constant divergence angle (~14°) over a broad energy range (10–50 MeV). Meanwhile, we demonstrate a ring-shaped X-ray source generated through bremsstrahlung radiation mechanism from electrons collision with channel walls, exhibiting a characteristic energy of 90 keV and emittance of 0.8 mm mrad. Three-dimensional simulations elucidate the underlying acceleration dynamics. It is found that elongated channels facilitate the formation of well-collimated electron beams. These results establish the foundation for applications of channel guided electrons and secondary radiation sources and represent a key step toward the controlled manipulation of particle sources in laser-driven plasmas.

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.

Controlling multiphase flow in disordered media is central to diverse practical contexts. Although nanoparticles have been widely utilised to modify surface wettability, factors governing their effects on dynamic displacement patterns remain unclear. Here, we identify the criterion for nanoparticle-induced wettability alteration during displacement by combining interfacial-scale wetting models, pore-scale microfluidic experiments and simulations. Motivated by striking contrasts in static wettability, we find that nanoparticle adsorption on solid surfaces affects displacement interfaces only when spreading of wetting films is pre-established, corresponding to corner-flow conditions. Displacement experiments under varying intrinsic wettability show that wetting-film development and non-aqueous droplet detachment are strengthened exclusively on moderately water-wet surfaces satisfying the corner-flow criterion. Investigations across designed porous structures with varying degrees of structural hierarchy validate the generality of the wettability criterion, while improvement in displacement efficiency diminishes with reduced hierarchy. The structural effect arises from variations in flow heterogeneity, with stronger heterogeneity simultaneously promoting film flow and ganglion mobilisation. The coupled impacts of wettability and structural conditions are summarised in an illustrative phase diagram delineating nanoparticle-tuned multiphase displacement. Our findings offer mechanistic insights into complex fluid flow in porous media and suggest optimised strategies for displacement control via nanoparticle suspensions.

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.

Radio recombination line (RRL) maser is a useful tool to study massive star formation regions with ionised gas close to new born massive stars. Masers often show sharp line profiles and/or extreme narrow widths, and high brightness temperatures. However, RRL masers were rarely detected only in several sources. Here we report the detection of sharp line profiles of the RRL H29$\alpha$, which can be interpreted as maser candidates, in two sources within W49A, a mini-starburst region in our Galaxy. These observations, conducted with high resolution ($\sim0.03''$) using the Atacama Large Millimeter/sub-millimeter Array (ALMA), reveal high brightness temperatures up to $\sim$9 000 K for H29$\alpha$ emission in another two sources, which might also be regarded as maser candidates. Additionally, suggestions for efficiently identifying RRL maser candidates are also provided.

Power scaling of neodymium (Nd)-doped single-frequency fiber lasers (SFFLs) operating at approximately 900 nm has been fundamentally constrained by dominant emission of approximately 1060 nm, with previous demonstrations limited to below 3 W. Here, we demonstrate a 910 nm single-mode Nd-doped SFFL system that achieves a record output power of over 30 W employing homemade Nd-doped silica fiber (NDF), while preserving exceptional 49 dB suppression of competing emission of approximately 1060 nm. The laser system originates from a distributed Bragg reflector single-frequency (SF) oscillator with 11 mW output power, which is subsequently amplified to 31.1 W through three polarization-maintaining (PM) amplification stages utilizing PM 10/125 μm NDF. To the best of our knowledge, this represents the highest power achieved for Nd-doped SFFLs in this spectral region. The output exhibits excellent beam quality (Mx2 = 1.03, My2 = 1.05) and narrow linewidth (10.2 kHz). These results validate that the homemade PM 10/125 μm NDF can be employed in intermediate and main amplifiers in all-fiber SF master oscillator power amplifier systems at approximately 900 nm.

Compressible wall-bounded turbulent flows exhibit complex mean profiles because of the pronounced compressibility effects and heat transfer. We propose a hybrid transformation framework to collapse compressible mean velocity and temperature profiles onto incompressible forms through scaling each layer by its effective transformation, with the underlying mapping functions discovered via a physics-informed symbolic regression (PISR) method. The hybrid velocity transformation incorporates an intrinsic compressibility correction for the buffer layer and a PISR-derived mapping function for the logarithmic layer. For temperature, we introduce a hybrid transformation that integrates the Mach-invariant-type transformation in the viscous sublayer and a novel PISR-derived scaling in the logarithmic layer. The performance of these transformations is evaluated across compressible turbulent boundary layers with free-stream Mach numbers ranging from 0.5 to 8 and wall-to-recovery-temperature ratios ranging from 0.25 to 1. The hybrid velocity transformation outperforms Griffin–Fu–Moin transformation for the transformed mean velocity profiles, with the mean integrated percent error across the dataset decreasing from 1.67 % to 0.96 %. The hybrid temperature transformation performs better than the Mach-invariant-type and Trettel–Larsson-type transformations for mean temperature profiles. Moreover, the inverse hybrid velocity and temperature transformations can effectively predict the compressible mean velocity and temperature profiles with only wall conditions.