Autonomous manoeuvre decision-making is essential for enhancing the survivability and operational effectiveness of unmanned aerial vehicles in high-risk and dynamic air combat scenarios. To address the limitations of traditional air combat decision-making methods in dealing with complex and rapidly changing environments, this paper proposes an autonomous air combat decision-making algorithm based on hybrid temporal difference error-reward prioritised experience replay with twin delayed deep deterministic policy gradient. This algorithm constructs a closed-loop learning system from environmental interaction to policy optimisation, addressing the key challenges of slow convergence and insufficient identification of critical tactical decisions in autonomous air combat. A hybrid priority metric leveraging reward backpropagation and temporal difference error filter is introduced to optimise the learning of high-value experiences while balancing sample diversity and the reuse of critical experiences. To reduce excessive trial and error in the initial phase, an integrated reward function combining task rewards and auxiliary guidance rewards is designed using the reward reshaping method to guide the agent on how to choose a manoeuvre strategy. Based on the established three-dimensional close-range air combat game model, simulation validations were conducted for both basic manoeuvre and expert system engagements. The results demonstrate that the proposed autonomous air combat manoeuvre decision-making algorithm exhibits higher learning efficiency and convergence stability. It can rapidly identify high-value manoeuvres and effectively formulate rational yet superior tactical strategies in the face of complex battlefield scenarios, demonstrating obvious benefits in enhancing combat effectiveness and tactical adaptability.

This paper studies a distributed fixed-time dynamic event-triggered formation control framework for a group of hypersonic gliding vehicles (GHGVs) suffering from internal uncertainties and non-affine properties. The main challenge is strong coupling of non-affine nonlinear dynamic with hypervelocity characteristics and multi-source uncertainties make it difficult to design the control protocol. Firstly, by integrating the distributed consensus control strategy, fractional order control theory and dynamic event-triggered mechanism, a framework of fixed-time formation control for GHGVs system is constructed. Secondly, to mitigate the issue of ‘explosion of complexity’ (EI), a fixed-time command filter (FCF) is proposed and a compensative strategy is formulated to tackle the impact of filtering errors. Thirdly, an additional auxiliary differential equation (ADE) is developed to decouple the control input from the status variable. Several radial base function neural networks (RBFNN) are utilised to handle the unknown internal uncertainties. Furthermore, a unique dynamic event-triggered mechanism (DTEM) is introduced for each follower, facilitating seamless transitions between two distinct dynamic threshold strategies. Analysis based on Lyapunov function illustrates that the output tracking error of followers exponentially converges to a small range within a fixed time, and Zeno behaviour is prevented. Finally, several numerical simulations are presented to demonstrate the practicability and meliority of the suggested approach.

This paper studies the adaptive distributed consensus tracking control framework for hypersonic gliding vehicles (HGVs) flying in tight formation. The system investigated in this paper is non-affine and subjected to multisource disturbances and mismatched uncertainties caused by a dramatically changing environment. Firstly, by refining the primary factors in the three-dimensional cluster dynamics, a non-affine closed-loop control system is summarised. Note that actual control is coupled with states, an additional auxiliary differential equation is developed to introduce additional affine control inputs. Furthermore, by employing the hyperbolic tangent function and disturbance boundary estimator, time-varying multisource disturbances can be handled. Several radial base function neural networks (RBFNNs) are utilised to approximate unknown nonlinearities. Furthermore, a generalised equatorial coordinate system is proposed to convert the longitudinal, lateral and vertical relative distances in the desired formation configuration into first-order consensus tracking error, such as latitude, longitude and height deviations. Analysis based on the Lyapunov function illustrates that variables are globally uniformly bounded, and the output tracking error of followers exponentially converges to a small neighbourhood. Finally, numerical simulations of equilibrium glide and spiral diving manoeuvers are provided to demonstrate the validity and practicability of the proposed approach.

Haemonchus contortus is a parasitic nematode that causes significant economic losses in ruminant livestock worldwide. In this study, we assessed the global genetic diversity and population structure of H. contortus using mitochondrial COX1 and ribosomal ITS2 sequences retrieved from the NCBI GenBank database. In total, 324 haplotypes of the COX1 and 72 haplotypes of the ITS2 were identified. The haplotype diversity values were all higher than 0.5, and the nucleotide diversity values were higher than 0.005. The Tajima’s D value for COX1 (−1.65634) was higher than that for ITS2 (−2.60400). Fu’s Fs, Fu and Li’s D (FLD), and Fu and Li’s F (FLF) values also showed high negative values, indicating a high probability of future population growth. In addition, the high fixation index (FST) value suggests significant genetic differentiation among populations. The haplotype networks of H. contortus populations based on COX1 sequences revealed clear geographic clustering, whereas ITS2 sequences showed more haplotype admixture across regions. The results of phylogenetic analyses were consistent with the haplotype networks. These findings highlighted that H. contortus populations exhibit significant genetic variation and are undergoing rapid population expansion, with clear genetic differences across geographic regions. This study established critical baseline data for future molecular epidemiology studies, which could guide region-specific parasite surveillance and targeted control strategies, thus helping to mitigate the risk of cross-border parasite transmission and drug resistance.

There are multiple equilibrium points in the launching and unfolding process of the multi-body aircraft. Different equilibrium points exhibit different stability characteristics and change with parameters such as connection method. The changes in stability characteristics can also lead to the inability of multi-body aircraft to achieve stable deployment. To solve these problems, the dynamic stability of multi-body aircraft during falling is analysed based on bifurcation theory in this paper. In this paper, Lagrange multiplier method is used to establish the multi-body dynamics model of the multi-body aircraft, and the curly spring torque model is added. In order to consider the coupling effect between the wings and the influence of the relative motion between the flight units on the aerodynamic force, the reference angle-of-attack, the reference sideslip angle, the relative attitude angle and the relative attitude angular velocity between the flight units were introduced as new variables to establish the aerodynamic model of the multi-body aircraft. Based on the equilibrium equations, the equilibrium curve of the two-body aircraft is obtained by using the joint stiffness coefficient as the continuous variable parameter. The stability of the equilibrium point domain on each equilibrium curve was analysed by using linearised theory. The dynamic characteristics of the launching and unfolding process of the two-body aircraft were analysed using bifurcation theory, and the stable domain was obtained regarding the initial folding angle and connection stiffness coefficient. The influence of initial folding angle and connection stiffness coefficient on the dynamic characteristics of the launching and unfolding process and the meaning of the stability domain were analysed through numerical simulation calculations. Finally, the correctness of the analysis conclusion was verified through experiments on the two-body aircraft, accumulating the technical foundation for subsequent research on high-altitude deployment.

Background: TERT promoter mutation (TPM) is an established biomarker in meningiomas associated with aberrant TERT expression and reduced progression-free survival (PFS). TERT expression, however, has also been observed even in tumours with wildtype TERT promoters (TP-WT). This study aimed to examine TERT expression and clinical outcomes in meningiomas. Methods: TERT expression, TPM status, and TERT promoter methylation of a multi-institutional cohort of meningiomas (n=1241) was assessed through nulk RNA sequencing (n=604), Sanger sequencing of the promoter (n=1095), and methylation profiling (n=1218). 380 Toronto meningiomas were used for discovery, and 861 external institution samples were compiled as a validation cohort. Results: Both TPMs and TERTpromoter methylation were associated with increased TERT expression and may represent independent mechanisms of TERT reactivation. TERT expression was detected in 30.4% of meningiomas that lacked TPMs, was associated with higher WHO grades, and corresponded to shorter PFS, independent of grade and even among TP-WT tumours. TERT expression was associated with a shorter PFS equivalent to those of TERT-negative meningiomas of one higher grade. Conclusions: Our findings highlight the prognostic significance of TERT expression in meningiomas, even in the absence of TPMs. Its presence may identify patients who may progress earlier and should be considered in risk stratification models.

The frequency responses of circulation control and separation control using mini-spoilers for loads attenuation on plunging swept and unswept wings were compared in a water tunnel study. At the pre-stall angle-of-attack, the effectiveness of the spoilers significantly diminishes with increasing reduced frequency of the plunging motion. For the leading-edge spoiler, this happens because the roll-up of the vorticity promotes flow reattachment and reduces the effectiveness of loads attenuation. For the trailing-edge spoiler, the effectiveness of lift attenuation also decreases with increasing reduced frequency, due to the shedding of leading-edge vortices and immersion of the trailing-edge spoiler in the separated flow. The decay of the frequency response for both types of spoilers is similar, implying that it is dictated by the flow separation near the leading edge of the wing in both cases. With increasing sweep angle of the wings, the spoilers’ effectiveness decreases significantly in comparison to the unswept wing. Strong spanwise flow develops for the leading-edge spoiler, which sheds a streamwise vortex, with the same direction of rotation as the wing-tip trailing vortex. This causes partial reattachment of the flow and reduction of the separation area behind the spoiler. With increasing reduced frequency, strong leading-edge vortices dominate the flow over the wing. The leading-edge vortices generate additional vortex lift and also cause the trailing-edge spoiler to be immersed in the massively separated flows. Both factors reduce the effectiveness of the spoilers.

We report on an experience with impostor research participants, people who misrepresent themselves, and identify characteristics that can be used by investigators to screen out such participants. We compare the responses of impostor and valid participants, showing that impostors meaningfully change qualitative study findings with implications for policy interventions or follow-on research informed by the study. It is important for investigators to be alert to the potential for impostor participants and plan their research accordingly.

Lactoferrin (LF), a sialylated iron-binding glycoprotein consisting of multiple sialic acid (Sia) residues attached to N-linked glycan chains, and studies have shown that both the iron and Sia are crucial for early neurodevelopment and cognition.(1) However, there is limited knowledge of the impacts of the iron saturation and sialylation in LF molecule on the early neurodevelopment and cognition. Objectives of the study were to explore the impacts and mechanisms of iron saturation and sialylation in LF molecule on early neurodevelopment and cognition. Maternal dietary intervention with native bovine LF (Native-LF), iron-free bovine LF (Apo-LF), or Sia-free bovine LF (Desia-LF) at a dose of 0.60 g/kg body weight per day was administered throughout the lactation period. Offspring pups were assessed for anxiety, learning, and memory through behavioral tests before being euthanized on postnatal day 63. Brain hippocampal tissue was then analyzed for polysialic acid (polySia), a marker of neurodevelopment and neuroplasticity.(1) The study protocol was approved by the Xiamen University Animal Ethics Committee (AE1640102). Our results showed that Apo-LF pups exhibited a 1.32-fold increase in total distance travelled in the arena compared to both Native-LF and Desia-LF groups, with the overall difference among the groups being statistically significant in the open field test (p = 0.008). Additionally, the frequency of central area entries in the Apo-LF group was 2.00-fold higher than in Desia-LF pups (p = 0.038) and 1.3-fold higher than in Native-LF pups, with a significant overall difference (p = 0.042). No significant differences in total distance travelled or central area entries were observed between Native-LF and Desia-LF groups (p > 0.05). These results suggest that Apo-LF pups demonstrated better anti-anxiety behaviors than both Native-LF and Desia-LF pups. In the Morris water maze test, Apo-LF pups spent significantly more time in the target quadrant compared to both Desia-LF (p = 0.019) and Native-LF pups (p = 0.0009), indicating enhanced short-term memory. Additionally, Apo-LF pups exhibited greater polySia-NCAM expression (1.2.95 ± 0.048) in the hippocampus, a marker associated with neuroplasticity and neurogenesis compared to both Native-LF and Desia-LF pups. We conclude that maternal supplementation with different types of lactoferrin during lactation supports improved learning and memory in offspring through distinct mechanisms, with sialylation playing a crucial role in neurocognitive development.

We present the first results from a new backend on the Australian Square Kilometre Array Pathfinder, the Commensal Realtime ASKAP Fast Transient COherent (CRACO) upgrade. CRACO records millisecond time resolution visibility data, and searches for dispersed fast transient signals including fast radio bursts (FRB), pulsars, and ultra-long period objects (ULPO). With the visibility data, CRACO can localise the transient events to arcsecond-level precision after the detection. Here, we describe the CRACO system and report the result from a sky survey carried out by CRACO at 110-ms resolution during its commissioning phase. During the survey, CRACO detected two FRBs (including one discovered solely with CRACO, FRB 20231027A), reported more precise localisations for four pulsars, discovered two new RRATs, and detected one known ULPO, GPM J1839 $-$10, through its sub-pulse structure. We present a sensitivity calibration of CRACO, finding that it achieves the expected sensitivity of 11.6 Jy ms to bursts of 110 ms duration or less. CRACO is currently running at a 13.8 ms time resolution and aims at a 1.7 ms time resolution before the end of 2024. The planned CRACO has an expected sensitivity of 1.5 Jy ms to bursts of 1.7 ms duration or less and can detect $10\times$ more FRBs than the current CRAFT incoherent sum system (i.e. 0.5 $-$2 localised FRBs per day), enabling us to better constrain the models for FRBs and use them as cosmological probes.

This paper develops a novel full-state-constrained intelligent adaptive control (FIAC) scheme for a class of uncertain nonlinear systems under full state constraints, unmodeled dynamics and external disturbances. The key point of the proposed scheme is to appropriately suppress and compensate for unmodeled dynamics that are coupled with other states of the system under the conditions of various disturbances and full state constraints. Firstly, to guarantee that the time-varying asymmetric full state constraints are obeyed, a simple and valid nonlinear error transformation method has been proposed, which can simplify the constrained control problem of the system states into a bounded control problem of the transformed states. Secondly, considering the coupling relationship between the unmodeled dynamics and other states of the controlled system such as system states and control inputs, a decoupling approach for coupling uncertainties is introduced. Thereafter, owing to the employed dynamic signal and bias radial basis function neural network (BIAS-RBFNN) improved on traditional RBFNN, the adverse effects of unmodeled dynamics on the controlled system can be suppressed appropriately. Furthermore, the matched and mismatched disturbances are reasonably estimated and circumvented by a mathematical inequality and a disturbance observer, respectively. Finally, numerical simulations are provided to demonstrate the effectiveness of the proposed FIAC strategy.

To investigate the flame acceleration to detonation in 2.0 and 0.5 mm planar glass combustion chambers, the experiments have been conducted utilising ethylene/oxygen mixtures at atmospheric pressure and temperature. The high-speed camera has been used to record the revolution of flame front and pressure inside the combustion chamber. Different equivalence ratios and ignition locations have been considered in the experiments. The results show that the detonation pressure in the 2 mm thick chamber is nearly three times of Chapman-Jouguet pressure, while detonation pressure in the 0.5 mm thick chamber is only 45.7% of the Chapman-Jouguet value at the stoichiometric mixture. This phenomenon is attributed to the larger pressure loss in the thinner chamber during the detonation propagation. As the value of equivalence ratio is 2.2, the detonation cannot be produced in the 2 mm thick chamber, while the detonation can be generated successfully in the 0.5 mm thick chamber. This phenomenon indicates that the deflagration is easily to be accelerated and transformed into the detonation, due to a larger wall friction and reflection. Besides, the micro-obstacle has been added into the combustor can shorten the detonation transition time and reduces the distance of the detonation transition.

The axisymmetric nozzle mechanism is the core part for thrust vectoring of aero engine, which contains complex rigid-flexible coupled multibody system with joints clearance and significantly reduces the efficiency in modeling and calculation, therefore the kinematics and dynamics analysis of axisymmetric vectoring nozzle mechanism based on deep neural network is proposed. The deep neural network model of the axisymmetric vector nozzle is established according to the limited training data from the physical dynamic model and then used to predict the kinematics and dynamics response of the axisymmetric vector nozzle. This study analyses the effects of joint clearance on the kinematics and dynamics of the axisymmetric vector nozzle mechanism by a data-driven model. It is found that the angular acceleration of the expanding blade and the driving force are mostly affected by joint clearance followed by the angle, angular velocity and position of the expanding blade. Larger joint clearance results in more pronounced fluctuations of the dynamic response of the mechanism, which is due to the greater relative velocity and contact force between the bushing and the pin. Since axisymmetric vector nozzles are highly complex nonlinear systems, traditional numerical methods of dynamics are extremely time-consuming. Our work indicates that the data-driven approach greatly reduces the computational cost while maintaining accuracy, and can be used for rapid evaluation and iterative computation of complex multibody dynamics of engine nozzle mechanism.

In this paper, a brand-new adaptive fault-tolerant non-affine integrated guidance and control method based on reinforcement learning is proposed for a class of skid-to-turn (STT) missile. Firstly, considering the non-affine characteristics of the missile, a new non-affine integrated guidance and control (NAIGC) design model is constructed. For the NAIGC system, an adaptive expansion integral system is introduced to address the issue of challenging control brought on by the non-affine form of the control signal. Subsequently, the hyperbolic tangent function and adaptive boundary estimation are utilised to lessen the jitter due to disturbances in the control system and the deviation caused by actuator failures while taking into account the uncertainty in the NAIGC system. Importantly, actor-critic is introduced into the control framework, where the actor network aims to deal with the multiple uncertainties of the subsystem and generate the control input based on the critic results. Eventually, not only is the stability of the NAIGC closed-loop system demonstrated using Lyapunov theory, but also the validity and superiority of the method are verified by numerical simulations.

Tribrachidium heraldicum is an Ediacaran body fossil characterized by triradial symmetry. Previous work has suggested that the anatomy of Tribrachidium was conducive to passive suspension feeding; however, these analyses used an inaccurate model and a relatively simple set of simulations. Using computational fluid dynamics, we explore the functional morphology of Tribrachidium in unprecedented detail by gauging how the presence or absence of distinctive anatomical features (e.g., apical pits and arms) affects flow patterns. Additionally, we map particle pathways, quantify deposition rates at proposed feeding sites, and assess gregarious feeding habits to more fully reconstruct the lifestyle of this enigmatic taxon. Our results provide strong support for interpreting Tribrachidium as a macroscopic suspension feeder, with the apical pits representing loci of particle collection (and possibly ingestion) and the triradial arms representing morphological adaptations for interrupting flow and inducing settling. More speculatively, we suggest that the radial grooves may represent ciliated pathways through which food particles accumulating in the wake of the organism were transported toward the apical pits. Finally, our results allow us to generate new functional hypotheses for other Ediacaran taxa with a triradial body plan. This work refines our understanding of the appearance of suspension feeding in shallow-water paleoenvironments, with implications for the radiation of Metazoa across the Ediacaran/Cambrian boundary.

The purpose of this study was to explore the electroencephalogram (EEG) features sensitive to situation awareness (SA) and then classify SA levels. Forty-eight participants were recruited to complete an SA standard test based on the multi-attribute task battery (MATB) II, and the corresponding EEG data and situation awareness global assessment technology (SAGAT) scores were recorded. The population with the top 25% of SAGAT scores was selected as the high-SA level (HSL) group, and the bottom 25% was the low-SA level (LSL) group. The results showed that (1) for the relative power of $\beta$1 (16–20Hz), $\beta$2 (20–24Hz) and $\beta$3 (24–30Hz), repeated measures analysis of variance (ANOVA) in three brain regions (Central Central-Parietal, and Parietal) × three brain lateralities (left, midline, and right) × two SA groups (HSL and LSL) showed a significant main effect for SA groups; post hoc comparisons revealed that compared with LSL, the above features of HSL were higher. (2) for most ratio features associated with $\beta$1 ∼ $\beta$3, ANOVA also revealed a main effect for SA groups. (3) EEG features sensitive to SA were selected to classify SA levels with small-sample data based on the general supervised machine learning classifiers. Five-fold cross-validation results showed that among the models with easy interpretability, logistic regression (LR) and decision tree (DT) presented the highest accuracy (both 92%), while among the models with hard interpretability, the accuracy of random forest (RF) was 88.8%, followed by an artificial neural network (ANN) of 84%. The above results suggested that (1) the relative power of $\beta$1 ∼ $\beta$3 and their associated ratios were sensitive to changes in SA levels; (2) the general supervised machine learning models all exhibited good accuracy (greater than 75%); and (3) furthermore, LR and DT are recommended by combining the interpretability and accuracy of the models.