In this paper, a single passage unsteady numerical simulation is carried out. Three different self-recirculating casing treatment structures with circumferential coverage ratios of 20%, 40% and 60% were designed. The calculation results show that as the circumferential coverage ratio increases, the stability enhancement ability of the self-recirculating casing also increases. Especially when the circumferential coverage ratio increases to 60%, the self-recirculating casing achieves the largest increase in stall margin, with an increase of 49.05%, but the decrease in the peak efficiency is 1.33%. An increase in the circumferential coverage ratio enhances the suction capacity of the self-recirculating casing. This enables it to better suppress the expansion of the leakage flow and reduce the degree of blockage within the passage. The self-recirculating casing can inhibit the occurrence of vortex breakdown in the tip passage. However, at the low flow rate point, it cannot effectively eliminate the interaction between the leakage streamlines. When the circumferential coverage ratio is relatively large, it can suppress the airflow separation phenomenon. The flow separation near the blade trailing edge and the mixing of the leakage flow within the tip passage are important reasons for the internal flow instability of the self-recirculating casing compressor.

Major public health emergencies have intensified, prompting some regions to implement stringent measures aimed at minimizing population movement, as seen in the response to incidents like the SARS outbreak in 2013 and the COVID-19 pandemic in 2020.1 Amidst the emphasis on public health crisis management, ensuring a stable supply of daily essentials like vegetables, meat, eggs, milk, and grains is imperative to maintain a sense of stability and order in daily life.2 The key challenge lies in the scientific and rational distribution of emergency supplies to ensure a consistent supply of various daily essentials within the public health event control area, which is an essential pragmatic concern.

Recently, data-driven methods have shown great promise for discovering governing equations from simulation or experimental data. However, most existing approaches are limited to scalar equations, with few capable of identifying tensor relationships. In this work, we propose a general data-driven framework for identifying tensor equations, referred to as symbolic identification of tensor equations (SITE). The core idea of SITE – representing tensor equations using a host–plasmid structure – is inspired by the multidimensional gene expression programming approach. To improve the robustness of the evolutionary process, SITE adopts a genetic information retention strategy. Moreover, SITE introduces two key innovations beyond conventional evolutionary algorithms. First, it incorporates a dimensional homogeneity check to restrict the search space and eliminate physically invalid expressions. Second, it replaces traditional linear scaling with a tensor linear regression technique, greatly enhancing the efficiency of numerical coefficient optimization. We validate SITE using two benchmark scenarios, where it accurately recovers target equations from synthetic data, showing robustness to noise and flexible expressive capability. Furthermore, SITE is applied to identify constitutive relations directly from molecular simulation data, which are generated without reliance on macroscopic constitutive models. It adapts to both compressible and incompressible flow conditions and successfully identifies the corresponding macroscopic forms, highlighting its potential for data-driven discovery of tensor equation.

Research has demonstrated that emotion modulates specificity in recollection of personally experienced events and the words individuals use during recollection reflect their psychological states. Here, we investigated the linguistic features of autobiographical memory (AM) of different specificity for different emotional events to address how emotion would modulate the psychological mechanisms underlying AM of different specificity. We analyzed 122 participants’ narratives of AM categorized as specific and general under happy, sad, angry, fearful and neutral cues. The use of three groups (emotional process, cognitive process and thinking style) of words was, respectively, compared between specific and general AM in each emotion condition. In retrieval of sad, angry and fearful events, general relative to specific AM contained more affective process words, notably negative words. General AM featured more cognitive process words than specific AM, regardless of emotion type (except neutral). When recalling happy events, general AM featured more analytic thinking words than specific AM, while in recollection of fearful events, general AM featured fewer such words than specific AM. General relative to specific AM about happy experiences contained more narrative thinking words. These findings suggest that the psychological mechanisms underlying top-down and bottom-up retrieval differ between particular types of emotion engaged in AM.

The debate on attention’s validity in cognitive psychology persists. However, attention remains essential beyond peripheral vision constraints, as it is a resource-limited process (Norman & Bobrow, 1975). The outright dismissal of attention proposed in the target article risks conceptual voids without superior alternatives. Instead, refining attention as a theoretical framework offers a pragmatic path for advancing cognitive research.

To investigate the stall mechanisms of a multi-stage axial compressor under different rotational speeds and identify the initial stall stages, this study focuses on a high-load nine-stage axial compressor, validated through experimental data. The results reveal that at 100% corrected rotational speed, flow instability is primarily triggered by corner separation in the front four stators (S1–S4). At 80% corrected rotational speed, the instability stems from the interaction between the first rotor (R1) tip leakage vortex and the main flow, coupled with the front four stators’ corner separation. Precise identification of initial stall locations in multi-stage axial compressors is imperative. The study first employs qualitative flow-field analysis to identify initial stall locations by comparing meridional mass flux variation contour maps and axial velocity iso-surfaces. The results show that the stall inception occurs at the S2 root under 100% corrected rotational speed, while at 80% corrected rotational speed, stall initiates simultaneously at both the S2 root and the R1 tip. Furthermore, an innovative three-dimensional flow blockage quantification method was established to systematically evaluate blockage severity within multi-stage blade passages. This approach utilises relative blockage variation metrics to quantitatively identify regions of rapid flow deterioration, achieving remarkable consistency with qualitative flow-field analysis. The qualitative and quantitative analysis results have been mutually corroborated. The proposed blockage quantification approach enables precise evaluation across stages without complex flow fields comparisons, allowing rapid identification of stall-initiating locations and supporting subsequent stability enhancement optimization.

Appropriate soil water and nitrogen (N) management strategies are critical for achieving sustainable agricultural development in drylands. Straw mulching has been used to improve crop yield and water use efficiency (WUE), but N management strategies may need to be adjusted from conventional practice. The current study investigated the interactive effects of N application rate (conventional and high N rate), N application frequencies (single, and split N in 2 – 3 applications) and seasonal conditions on wheat population density dynamics, yield, harvest index (HI), grain protein content, water- and N-use efficiency, and residual soil N under straw mulching on the Loess Plateau of China. Nitrogen rate had no effect on yield, HI, WUE and grain protein content, but high N rate resulted in lower grain weight and nitrogen partial factor productivity (PFPN), and higher soil N residue. Splitting N applications significantly improved grain yield (7%), HI (9%), grain protein content (5%), PFPN and N harvest index, along with a reduction in soil N residue, compared to single application. However, there was no difference in above traits between split-N in 2 and 3 applications. Conventional N rate (vs. high N rate) and split N application (vs. single application) both alleviated the negative correlation between grain yield and grain protein content, and split N application increased grain N removal per unit yield compared to single N application. It is concluded that conventional N rate combined with split application in two doses, is suitable for straw mulching in drylands of the Loess Plateau, China.

Transonic buffet is a complex and strongly nonlinear unstable flow sensitive to variations in the incoming flow state. This poses great challenges for establishing accurate-enough reduced-order models, limiting the application of model-based control strategies in transonic buffet control problems. To address these challenges, this paper presents a time-variant modelling approach that incorporates rolling sampling, recursive parameter updating and inner iteration strategies under dynamic incoming flow conditions. The results demonstrate that this method successfully overcomes the difficulty in designing appropriate training signals and obtaining unstable steady base flow. Additionally, it improves the global predictive capability and identification efficiency of linear models for nonlinear flow-system responses by more than one order of magnitude. Furthermore, two adaptive control strategies – minimum variance control and generalised predictive control – are validated as effective based on the time-variant reduced-order model through numerical simulations of the transonic buffet flow over the NACA 0012 aerofoil. The adaptive controllers effectively regulate the unstable eigenvalues of the flow system, achieving the desired control outcomes. They ensure that the shock wave buffet phenomenon does not recur after control is applied, and that the actuator deflection, specifically the trailing-edge flap, returns to zero. Moreover, the control results further confirm the global instability essence of transonic buffet flow from a control perspective, thereby deepening the cognition of this nonlinear unstable flow.

Understanding the flow behaviour of wet granular materials is essential for comprehending the dynamics of numerous geological and physical phenomena, but remains a significant challenge, especially the transition of these flow regimes. In this study, we perform a series of rotating drum experiments to systematically investigate the dynamic observables and flow regimes of wet mono-dispersed particles. Two typical continuous flows including rolling and cascading regimes are identified and analysed, concentrating on the impact of fluid density and rotation speed. The probability density functions of surface angles, $\theta _{\textit{top}}$ and $\theta _{\textit{lo}w\textit{er}}$, reveal distinct patterns for these two flow regimes. A morphological parameter thus proposed, termed angle divergence, is used to characterise the rolling–cascading regime transition quantitatively. By integrating quantitative observables, we construct the flow phase diagram and flow curve to delineate the transition rules governing these regimes. Notably, the resulting nonlinear phase boundary demonstrates that higher fluid densities significantly enhance the likelihood of the system transitioning into the cascading regime. This finding is further supported by corresponding variations in flow fluctuations. Our results provide new insights into the fundamental dynamics of wet granular matter, offering valuable implications for understanding the complex rheology of underwater landslides and related phenomena.

In the fields of meal-assisting robotics and human–robot interaction (HRI), real-time and accurate mouth pose estimation is critical for ensuring interaction safety and improving user experience. The complexity arises from the diverse opening degrees of mouths, variations in orientation, and external factors such as lighting conditions and occlusions, which pose significant challenges for real-time and accurate posture estimation of mouths. In response to the above-mentioned issues, this paper proposes a novel method for point cloud fitting and posture estimation of mouth opening degrees (FP-MODs). The proposed method leverages both RGB and depth images captured from a single viewpoint, integrating geometric modeling with advanced point cloud processing techniques to achieve robust and accurate mouth posture estimation. The innovation of this work lies in the hypothesis that different states of mouth openings can be effectively described by distinct geometric shapes: closed mouths are modeled by spatial quadratic surfaces, half-open mouths by spatial ellipses, and fully open mouths by spatial circles. Then, based on these hypotheses, we developed algorithms for fitting geometric models to point clouds obtained from mouth regions, respectively. Specifically, for the closed mouth state, we employ an algorithm based on least squares optimization to fit a spatial quadratic surface to the point cloud data. For the half-open or fully open mouth states, we combine inverse projection methods with least squares fitting to model the contour as a spatial ellipse and circle, respectively. Finally, to evaluate the effectiveness of the proposed FP-MODs method, extensive actual experiments were conducted under varying conditions, including different orientations and various types of mouths. The results demonstrate that the proposed FP-MODs method achieves high accuracy and robustness. This study can provide a theoretical foundation and technical support for improving HRI and food delivery safety in the field of robotics.

We consider spline-based additive models for estimation of conditional treatment effects. To handle the uncertainty due to variable selection, we propose a method of model averaging with weights obtained by minimizing a J-fold cross-validation criterion, in which a nearest neighbor matching is used to approximate the unobserved potential outcomes. We show that the proposed method is asymptotically optimal in the sense of achieving the lowest possible squared loss in some settings and assigning all weight to the correctly specified models if such models exist in the candidate set. Moreover, consistency properties of the optimal weights and model averaging estimators are established. A simulation study and an empirical example demonstrate the superiority of the proposed estimator over other methods.

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.

In the field of parafoil airdrop path planning, the inherent complexity and time-sensitive nature of mission requirements necessitate rapid path generation through low-order mathematical models that approximate the system’s true dynamics. This study presents a novel sparse identification framework for constructing a parafoil path planning approximate model. Leveraging high-fidelity 9-degree-of-freedom (9 DOF) dynamic simulation data as training inputs, our method identifies simple nonlinear relationships between 3D positional coordinates (for spatial targeting) and yaw angle (for directional control), which are critical path planning parameters. Compared to conventional 4 DOF models, experimental validation using field airdrop data reveals that the proposed sparse model achieves enhanced predictive accuracy while maintaining computational efficiency. Quantitative analysis demonstrates reductions in root mean square error (RMSE) by approximately 12.96% (horizontal position), 54.44% (height) and 37.96% (yaw angle). The efficacy is further confirmed through successful fixed-point homing across diverse initial deployment scenarios, underscoring its potential for parafoil path planning.

Paleontology provides insights into the history of the planet, from the origins of life billions of years ago to the biotic changes of the Recent. The scope of paleontological research is as vast as it is varied, and the field is constantly evolving. In an effort to identify “Big Questions” in paleontology, experts from around the world came together to build a list of priority questions the field can address in the years ahead. The 89 questions presented herein (grouped within 11 themes) represent contributions from nearly 200 international scientists. These questions touch on common themes including biodiversity drivers and patterns, integrating data types across spatiotemporal scales, applying paleontological data to contemporary biodiversity and climate issues, and effectively utilizing innovative methods and technology for new paleontological insights. In addition to these theoretical questions, discussions touch upon structural concerns within the field, advocating for an increased valuation of specimen-based research, protection of natural heritage sites, and the importance of collections infrastructure, along with a stronger emphasis on human diversity, equity, and inclusion. These questions offer a starting point—an initial nucleus of consensus that paleontologists can expand on—for engaging in discussions, securing funding, advocating for museums, and fostering continued growth in shared research directions.