The improvement of the accuracy and real-time performance of sector traffic flow prediction is of great significance to air traffic management decision-making. Sectors operate under complex spatial structures and time dimensions. Some neural network methods adopt sequence order to gradually transmit information, which makes it difficult to achieve complete parallel training. Not only does it take too long to train, resulting in low training efficiency, but it is also easy to lose the effective correlation information of long sequence data. To this end, a sector traffic flow prediction method based on attention-improved graph convolutional transformer (AGC-T) network is proposed to improve the current traffic prediction problem for sectors. First, the graph structure information and historical traffic data of the sector are input into the graph convolutional network improved based on the attention mechanism to fully capture the spatial relationship with sectors as nodes. Combined with the transformer’s multi-head self-attention mechanism, it can directly focus on the sequence data at any position without gradually transmitting information. Not only does it improve efficiency through parallel training, but the encoder-decoder structure can also mine the information features in the traffic data, focus on the traffic data features of key nodes and more accurately predict sector traffic. Finally, the operation traffic data of sectors in typical areas in central and southern China are taken as an example to analyse the model. The results show that compared with other prediction models, the AGC-T model $RSME$, $MAE$ and ${R^2}$ are 45.16%, 46.78% and 2.63% higher than the GCN model in the 15-min single-day traffic prediction task, and 41.74%, 35.27% and 1.20% higher than the GRU model. In the single-week traffic prediction task, $RSME$, $MAE$ and ${R^2}$ are 37.12%, 40.54% and 3.55% higher than the GCN model, and 35.15%, 35.17% and 0.65% higher than the GRU model, respectively, showing better prediction performance. This study will help air navigation service providers (ANSP) to make sector traffic predictions more accurately, thereby implementing more scientific and reasonable traffic management measures.

Single-cell tornado-like vortices (TLVs) exhibit periodic wandering fluctuations around the time-averaged vortex core, a phenomenon known as vortex wandering, which constitutes the most prominent periodic behaviour in such flows. The coupling between vortex motion and wandering creates complex swirl dynamics, posing significant analytical challenges. However, the limited availability of experimental studies on vortex wandering decomposition hampers a deeper understanding of this phenomenon. To address this gap, a tornado simulator was designed to generate a controllable single-cell TLV, and high-frequency velocity data were obtained using particle image velocimetry. A sparsity-promoting dynamic mode decomposition (sp-DMD) method was developed to decouple coherent structures and analyse dynamic characteristics. Results show that as the swirl ratio increases, the vortex structure becomes more diffuse, with significant reductions in intensity. Vortex wandering is present across all swirl conditions, with its periodicity strongly modulated by the swirl ratio. Importantly, sp-DMD identified two primary modes, the time-averaged mode (first mode), representing the dominant rotational vortex motion, and the vortex-wandering-dominated modes (second and third conjugate modes), which correspond to persistent periodic velocity fluctuations and contribute the most significant pulsations. These modes exhibit a pair of oppositely rotating vortices symmetrically revolving around the central flow axis. Visualisations of the Q criterion reveal a symmetric dipole pattern. This suggests that rotational and shear effects are likely responsible for the periodic movement of the vortex core. Furthermore, as the swirl ratio increases, the energy of the vortex-wandering-dominated modes diminishes, and motion transitions from high-energy, organised dynamics to low-energy, disordered behaviour.

Previous research has mainly explored the relationship between bilingual language control and domain-general cognitive control through behavioral correlations, often revealing epiphenomenal links rather than causality. This study utilizes transcranial magnetic stimulation (TMS) to investigate the causal roles of the left inferior frontal gyrus (LIFG) and left middle temporal gyrus (LMTG) in 33 unbalanced Chinese-English bilinguals. Continuous theta burst stimulation was applied in separate sessions to decrease cortical excitability, with vertex stimulation as a control. LIFG stimulation significantly increased switching costs in nonverbal switching tasks, highlighting its role in domain-general cognitive control. LMTG stimulation did not affect switching or mixing costs in language or nonverbal switching tasks, suggesting no causal involvement, but it reduced reaction times (RTs) during language switching tasks, underscoring its specialization in language processing. These findings highlight distinctions between the neural mechanisms of bilingual language control and domain-general cognitive control, particularly in the LIFG.

The evolution mechanisms and suppression strategy of the Richtmyer–Meshkov instability (RMI) at heavy–light interfaces with varying Atwood numbers accelerated by two co-propagating shock waves are investigated through theoretical analysis and experimental evaluation. Existing models describing the complete evolution of once-shocked interfaces and the linear growth of twice-shocked interfaces are examined across low, moderate and high Atwood number regimes, and further refined based on detailed analyses of their limitations. Furthermore, an analytical model for describing the complete evolution of a twice-shocked interface (DS model) is developed through a comprehensive consideration of the shock-compression, start-up, linear and weakly nonlinear evolution processes. The combination of the refined models and DS model enables, for the first time, an accurate prediction of the complete evolution of interfaces subjected to two co-propagating shock waves. Building upon this, the parameter conditions required to manipulate the RMI with varying Atwood numbers are identified. Verification experiments confirm that suppressing the RMI growth at interfaces with various Atwood numbers via a same-side reshock is feasible and predictable. The present study may shed some light on strategies to suppress hydrodynamic instabilities in inertial confinement fusion through integrated adjustment of material densities and shock timings.

To address the complexity and excessive reliance on expert experience in tuning fuzzy Proportional-Integral-Derivative (PID) controller parameters, this study proposes a variable-rate spraying control system that integrates an improved Beetle Antennae Search (IBAS) algorithm with fuzzy PID control. To evaluate the feasibility of the system, a mathematical transfer function of the variable-rate spraying system was constructed, and a flow control simulation platform was established for simulation analysis. To overcome the limitations of conventional BAS, which is prone to premature convergence and limited search precision, the IBAS algorithm was developed. The improvements include a hybrid disturbance strategy to enhance individual search capability and a simulated annealing mechanism to prevent the algorithm from being trapped in local optima. Using the IBAS algorithm, the proportional and quantization factors of the fuzzy PID controller were optimized offline to obtain the optimal parameters. The IBAS-fuzzy PID controller was then compared in simulation with conventional PID, fuzzy PID, and BAS-optimized fuzzy PID controllers. The simulation results demonstrated that the IBAS-fuzzy PID algorithm achieved higher flow control accuracy than existing methods. To further validate the effectiveness of the improved algorithm under practical conditions, field experiments were conducted. The results indicated that the IBAS-optimized fuzzy PID controller outperformed the three other control methods in terms of flow control accuracy. Overall, both simulation and field results confirm that the proposed IBAS algorithm for fuzzy PID parameter optimization significantly enhances response speed, control precision, and overshoot reduction, providing a novel approach and potential application for variable-rate spraying technology.

High-resolution particle image velocimetry (PIV) particle-to-velocity analyses using small interrogation areas (IAs) often require substantial processing time. To overcome this limitation, a generative adversarial network (GAN)-based model is proposed to achieve spatio-temporal super-resolution (SR) reconstruction from low-resolution PIV data with large IAs, thereby significantly reducing post-processing time. Time-resolved PIV measurements of plasma-induced vortex flows, covering vortex formation, growth, transition and breakdown stages, are employed to train and evaluate the model with multi-scale vortical structures. By sequentially constructing spatial and temporal datasets, the GAN-based model enables reliable SR reconstruction at different scaling factors. Reconstruction accuracy is systematically assessed using time-averaged, instantaneous and phase-averaged velocity fields. At SR factors of $\times$4 and $\times$8, the reconstructed fields closely match high-resolution references, effectively capturing both fluctuating velocities and small-scale vortical structures. In contrast, $\times$16 reconstructions exhibit diminished accuracy due to the loss of fine-scale information from highly downsampled inputs. For time-averaged fields, high-resolution reconstructions reliably capture plasma jet characteristics at all SR factors. To enhance generalisation, transfer learning is introduced to fine tune the parameters of SR-related layers in the generator, enabling accurate reconstructions under varying vortex dynamics. In addition, the efficiency gains in PIV particle-to-velocity analysis and the fundamental limitations on achievable SR factors imposed by spatio-temporal data correlations are discussed. This study demonstrates that GAN-based spatio-temporal SR models offer a promising approach to accelerate PIV analyses while maintaining high reconstruction fidelity with diverse flow conditions.

Emerging labour crises highlight the detrimental impact of overtime work cultures, such as the 996 working system, which violates the Labour Law of China and mirrors modern labour slavery. Simultaneously, despite China’s Three-Children Policy aimed at increasing national labour force growth, the national fertility rate has remained persistently low over the past decade. Workers require family time and financial security to plan for having children. Changes in family structures and fertility expectations, shaped by social pressures and the government’s advocacy, heavily impact labourers’ household financial behaviour. In response to a high-pressure overwork environment, labourers adopt conservative financial strategies to safeguard their families’ well-being and birth plans. This cautious approach often involves avoiding digital financial tools associated with riskier investments. This study examines the intersection of labour overwork, fertility, and the adoption of digital finance in shaping Chinese families’ investments. Drawing on the Theory of Planned Behaviour, this study analyses the panel data from 7,582 family observations in China between 2018 and 2020. The findings reveal that although digital finance positively influences household financial investment, the 996 work system acts as a moderator to shape this relationship negatively. Moreover, fertility in households further weakens this relationship. These findings provide critical theoretical insights into the dynamics of labour history by portraying a modern slavery picture of overworked labourers and their families in China: too exhausted and financially strained to have babies. It offers practical insights for policymakers aiming to improve labour policies, fertility rates, and household financial resilience.

Investigations into the effects of polymers on small-scale statistics and flow patterns were conducted in a turbulent von Kármán swirling (VKS) flow. We employed the tomographic particle image velocimetry technique to obtain full information on three-dimensional velocity data, allowing us to effectively resolve dissipation scales. Under varying Reynolds numbers ($R_\lambda =168{-}235$) and polymer concentrations ($\phi =0{-}25\ {\textrm{ppm}}$), we measured the velocity gradient tensor (VGT) and related quantities. Our findings reveal that the ensemble average and probability density function (PDF) of VGT invariants, which represent turbulent dissipation and enstrophy along with their generation terms, are suppressed as polymer concentration increases. Notably, the joint PDFs of the invariants of VGT, which characterise local flow patterns, exhibited significant changes. Specifically, the third-order invariants, especially the local vortex stretching, are greatly suppressed, and strong events of dissipation and enstrophy coexist in space. The local flow pattern tends to be two-dimensional, where the eigenvalues of the rate-of-strain tensor satisfy a ratio $1:0:-1$, and the vorticity aligns with the intermediate eigenvector of the rate-of-strain tensor, while it is perpendicular to the other two. We find that these statistics observations can be well described by the vortex sheet model. Moreover, we find that these vortex sheet structures align with the symmetry axis of the VKS system, and orient randomly in the horizontal plane. Further investigation, including flow visualisation and conditional statistics on vorticity, confirms the presence of vortex sheet structures in turbulent flows with polymer additions. Our results establish a link between single-point statistics and small-scale flow topology, shedding light on the previously overlooked small-scale structures in polymeric turbulence.

The recent discovery of polymer diffusive instability (PDI) by Beneitez et al. (2023 Phys. Rev. Fluids 8, L101901), poses challenges in implementing artificial conformation diffusion (ACD) in transition simulations of viscoelastic wall-shear flows. In this paper, we demonstrate that the unstable PDI is primarily induced by the conformation boundary conditions additionally introduced in the ACD equation system, which could be eliminated if a new set of conformation conditions is adopted. To address this issue, we begin with an asymptotic analysis of the PDI within the near-wall thin diffusive layer, which simplifies the complexity of the instability system by reducing the number of the controlling parameters from five to zero. Then, based on this simplified model, we construct a stable asymptotic solution that minimises the perturbations in the wall sublayer. From the near-wall behaviour of this solution, we derive a new set of conformation boundary conditions, prescribing a Neumann-type condition for its streamwise stretching component, $c_{11}$, and Dirichlet-type conditions for all the other conformation components. These boundary conditions are subsequently validated within the original ACD instability system, incorporating both the Oldroyd-B and the finitely extensible nonlinear elastic Peterlin constitutive models. Finally, we perform direct numerical simulations based on the traditional and the new conformation conditions, demonstrating the effectiveness of the latter in eliminating the unstable PDI. Importantly, this improvement does not affect the calculations of other types of instabilities. Therefore, this work offers a promising approach for achieving reliable polymer-flow simulations with ACD, ensuring both numerical stability and accuracy.

Visual Simultaneous Localization and Mapping (vSLAM) is essentially limited by the static world assumption, which makes its application in dynamic environments challenging. This paper proposes a robust vSLAM system, RFN-SLAM, which is based on ORB-SLAM3 and does not require preset dynamic labels and weighted features to process dynamic scenes. In the feature extraction stage, an enhanced efficient binary image BAD descriptor is used to improve the accuracy of static feature point matching. Through the improved RT-DETR target detection network and FAST-SAM instance segmentation network, RFN-SLAM obtains semantic information and uses a novel dynamic box detection algorithm to identify and eliminate the feature points of dynamic objects. When optimizing the pose, the static feature points are weighted according to the dynamic information, which significantly reduces the mismatch and improves the accuracy of positioning. Meanwhile, 3D rendering of the neural radiation field is used to remove dynamic objects and render them. Experiments were conducted on the TUM RGB-D dataset, Bonn dataset, and self-collected dataset. The results show that in terms of positioning accuracy, RFN-SLAM significantly outperforms ORB-SLAM3 in dynamic environments. It also achieves more accurate positioning than other advanced dynamic SLAM methods and successfully realizes accurate 3D reconstruction of static scenes. In addition, on the premise of ensuring accuracy, the real-time performance of RFN-SLAM is effectively guaranteed.

Older adults commonly experience declines in cognitive control, which significantly impacts their well-being. Although intensive language training, particularly interpreting, holds potential for mitigating these declines, its efficacy remains largely unexplored. Based on previous findings in the literature (especially our theoretical framework on interpreting), we designed a 24-hour programme of Between-Dialect Interpreting Training (BIT). Using a pretest-intervention-posttest design, we evaluated the efficacy of the BIT (over 8 weeks) against a control group on general cognitive ability (MoCA) and core cognitive control functions – working memory (via listening span and digit backward tasks), interference control (via Stroop and Flanker tasks) and cognitive flexibility (via colour-shape task and WCST). Results demonstrated notable between-group differences favouring the BIT, with significant improvements in listening span, Stroop effect and Stroop global RT, colour-shape switch cost and marginal improvements in digit backward score and MoCA. The implications of how language training promotes cognitive health during ageing are discussed.

Artificial intelligence (AI) is revolutionizing the way firms pursue technological diversification (TD), yet its distinct effects on related and unrelated diversification remain insufficiently explored. Based on the knowledge-based view, this study examines the distinct effects of AI on related and unrelated TD to elucidate AI’s specific role in facilitating both the optimization of existing knowledge and the exploration of new domains. Using a multi-period difference-in-differences model and panel data from China’s listed manufacturing firms (2013–2022), our empirical analysis demonstrates that AI significantly promotes firm TD, particularly in unrelated TD. Additionally, we identify that core-technology competence strengthens the positive effect of AI on unrelated TD, while knowledge stocks weaken it. These results contribute to the literature on TD by underscoring the role of AI. Practically, the study offers actionable insights for managers to harness AI in balancing exploration and exploitation within their TD strategies.