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.

Choline is an essential nutrient required in increased amounts during periods of rapid growth such as pregnancy and early life(1). It is a precursor for acetylcholine synthesis, a key neurotransmitter involved in muscle coordination and memory, and betaine, a major methyl donor. Choline is also a component of phosphatidylcholine, a phospholipid that makes up to 50% of neural tissue, most of which is accrued in late pregnancy and early life. Choline can be synthesised in the liver; however, de novo synthesis is not sufficient to meet needs and exogenous choline must come from diet. Australia’s National Health and Medical Research Council (NHMRC) has established Adequate Intakes (AI) for choline. During pregnancy and lactation, the AI for choline is set at 440 and 550 mg/d. For young children 0–3 years of age, the AI is set at 125–200 mg/day. We and others have shown that pregnant and lactating Australian women have low choline intakes with few exceeding their AI (1–40%). There are no data on the choline intakes of children under 2 years. In 2021, we published the first ever Australia-wide study on nutrient intakes in children under two years, and, in 2024, updated it to include choline. Using a 24-hour food record with repeats in a subsample of the population (~30%), we estimated usual choline intake distributions and its dietary sources in n = 761 children 6–24 months. Average choline intakes for infants 6–12 months and toddlers 12–24 months were 142 ± 1.9 and 181 ± 1.2 mg/day, respectively. Only one third of infants and one quarter of toddlers exceeded their respective AIs of 125 and 200 mg/day. Breastfeeding rates were high with 78% of infants and 44% of toddlers still receiving breastmilk. In both groups, breastmilk was the leading source of choline contributing 56% and 32% among consumers(2). Animal-source foods (meat, chicken, fish, and eggs) rich in choline and other essential nutrients were consumed by less than one third of children and in small amounts. Given choline’s role in neurodevelopment, the low intakes observed in pregnant and lactating women and young children suggest dietary intakes may need improvement. Moreover, the impact of low choline intake on neurodevelopmental outcomes remains unknown and warrants further investigation.

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.

The rising demand for air traffic will inevitably result in a surge in both the number and complexity of flight conflicts, necessitating intelligent strategies for conflict resolution. This study addresses the critical challenges of scalability and real-time performance in multi-aircraft flight conflict resolution by proposing a comprehensive method that integrates a priority ranking mechanism with a conflict resolution model based on the Markov decision process (MDP). Within this framework, the proximity between aircraft in a multi-aircraft conflict set is dynamically assessed to establish a conflict resolution ranking mechanism. The problem of multi-aircraft conflict resolution is formalised through the MDP, encompassing the design of state space, discrete action space and reward function, with the transition function implemented via simulation prediction using model-free methods. To address the positional uncertainty of aircraft in real-time scenarios, the conflict detection mechanism introduces the aircraft’s positional error. A deep reinforcement learning (DRL) environment is constructed incorporating actual airspace structures and traffic densities, leveraging the Actor Critic using Kronecker-factored Trust Region (ACKTR) algorithm to determine resolution actions. The experimental results indicate that with 20–30 aircraft in the airspace, the success rate can reach 94% for the training set and 85% for the test set. Furthermore, this study analyses the impact of varying aircraft numbers on the success rate within a specific airspace scenario. The outcomes of this research provide valuable insights for the automation of flight conflict resolution.

Background: Efgartigimod is a human IgG1 antibody Fc fragment recently approved by Health Canada for patients with acetylcholine receptor antibody positive (AChR-Ab+) generalized myasthenia gravis (gMG). We assessed cost-effectiveness of efgartigimod vs chronic IVIg for adult patients with AChR-Ab+ gMG. Methods: A Markov model estimated costs (treatment and administration, disease monitoring, complications from chronic corticosteroid use, exacerbation and crisis management, adverse events, end-of-life care) and benefits (quality-adjusted life-years [QALYs]). The analysis was conducted from the Canadian healthcare system perspective. Health state transition probabilities were estimated using data from ADAPT, ADAPT+, and a network meta-analysis comparing efgartigimod with chronic IVIg. Utility values were obtained from MyRealWorld MG. Patients with MG-ADL ≥5 who did not die/discontinue were assumed to receive treatment every 4 weeks or every 3 weeks over the lifetime horizon. Results: Over the lifetime horizon, efgartigimod and chronic IVIg were predicted to have total discounted QALYs of 16.80 and 13.35, and total discounted costs of $1,913,294 and $2,170,315, respectively. Efgartigimod dominated chronic IVIg with incremental QALYs of 3.45 and cost savings of $257,020 over the lifetime horizon. Conclusions: Efgartigimod may provide greater benefit at lower costs than chronic IVIg for Canadian patients with AChR-Ab+ gMG, with substantial healthcare system savings over the lifetime horizon.

Cryogenic electron tomography (cryoET) is capable of determining in situ biological structures of molecular complexes at near-atomic resolution by averaging half a million subtomograms. While abundant complexes/particles are often clustered in arrays, precisely locating and seamlessly averaging such particles across many tomograms present major challenges. Here, we developed TomoNet, a software package with a modern graphical user interface to carry out the entire pipeline of cryoET and subtomogram averaging to achieve high resolution. TomoNet features built-in automatic particle picking and three-dimensional (3D) classification functions and integrates commonly used packages to streamline high-resolution subtomogram averaging for structures in 1D, 2D, or 3D arrays. Automatic particle picking is accomplished in two complementary ways: one based on template matching and the other using deep learning. TomoNet’s hierarchical file organization and visual display facilitate efficient data management as required for large cryoET datasets. Applications of TomoNet to three types of datasets demonstrate its capability of efficient and accurate particle picking on flexible and imperfect lattices to obtain high-resolution 3D biological structures: virus-like particles, bacterial surface layers within cellular lamellae, and membranes decorated with nuclear egress protein complexes. These results demonstrate TomoNet’s potential for broad applications to various cryoET projects targeting high-resolution in situ structures.

A rapid nonlinear aeroelastic framework for the analysis of the highly flexible wing with distributed propellers is presented, validated and applied to investigate the propeller effects on the wing dynamic response and aeroelastic stability. In the framework, nonlinear beam elements based on the co-rotational method are applied for the large-deformation wing structure, and an efficient cylinder coordinate generation method is proposed for attached propellers at different position. By taking advantage of the relatively slow dynamics of the high-aspect-ratio wing, propeller wake is modeled as a quasi-steady skewed vortex cylinder with no updating process to reduce the high computational cost. Axial and tangential induced velocities are derived and included in the unsteady vortex lattice method. For the numerical cases explored, results indicate that large deformation causes thrust to produce wing negative torsion which limits the displacement oscillation, and slipstream mainly increases the response values. In addition, an improvement of flutter boundary is found with the increase of propeller thrust while slipstream brings a destabilising effect as a result of the increment of dynamic pressure and local lift. The great potential of distributed propellers in gust alleviation and flutter suppression of such aircraft is pointed out and the method as well as conclusions in this paper can provide further guidance.

A multi-agent deep reinforcement learning (DRL)-based model is presented in this study to reconstruct flow fields from noisy data. A combination of reinforcement learning with pixel-wise rewards, physical constraints represented by the momentum equation and the pressure Poisson equation, and the known boundary conditions is used to build a physics-constrained deep reinforcement learning (PCDRL) model that can be trained without the target training data. In the PCDRL model, each agent corresponds to a point in the flow field and learns an optimal strategy for choosing pre-defined actions. The proposed model is efficient considering the visualisation of the action map and the interpretation of the model operation. The performance of the model is tested by using direct numerical simulation-based synthetic noisy data and experimental data obtained by particle image velocimetry. Qualitative and quantitative results show that the model can reconstruct the flow fields and reproduce the statistics and the spectral content with commendable accuracy. Furthermore, the dominant coherent structures of the flow fields can be recovered by the flow fields obtained from the model when they are analysed using proper orthogonal decomposition and dynamic mode decomposition. This study demonstrates that the combination of DRL-based models and the known physics of the flow fields can potentially help solve complex flow reconstruction problems, which can result in a remarkable reduction in the experimental and computational costs.

The boundary layer thickness on a compressor blade suction surface increases rapidly under a adverse pressure gradient and even separates from the blade surface. This paper proposes a novel method for developing the slot inside the blade, with the inlet of the slot located at the leading edge of the blade and the outlet located at the suction surface, using the momentum of the incoming flow to form a high velocity jet to control the boundary layer on the suction surface. For a plane cascade with a diffusion factor of 0.45, the effects of the main slot parametres (such as the shape of the slot and the positions of the slot inlet and outlet) on the flow in the slot, the flow field and the aerodynamic performance of the cascade were investigated with a numerical method. When the aerodynamic performance of cascades with slotted and unslotted blades was compared, it was found that a reasonable slot structure can effectively inhibit the development of the boundary layer on the blade suction surface and greatly improve the aerodynamic performance of the cascade. Based on the influence of the slot parametres of the above cascade, the slot of a plane cascade with a diffusion factor of 0.60 was designed. The numerical calculation results show that the slotted cascade with a diffusion factor of 0.60 outperformed the slotted cascade with a diffusion factor of 0.45. This result showed that the higher the cascade load, the greater the performance improvement from slotting. Furthermore, the unslotted and slotted cascades were tested, and the test results agreed well with the calculations. The aerodynamic performance of the slotted cascade was better than that of the unslotted cascade, which verifies the accuracy of the calculation method and the feasibility of blade slotting for suppressing the development of boundary layers on suction surfaces and reducing flow loss.

The pulsed jet is a novel and effective active mixing enhancement approach. For the transverse pulsed jet in the supersonic crossflow, the frequency influence is investigated using the three-dimensional Reynolds-averaged Navier–Stokes (RANS) equations coupled with the SST k-ω turbulence model. The averaged flow field properties of the pulsed jet are better than those of the steady jet when considering mixing efficiency and jet penetration depth, especially for the case with the pulsed frequency being 50kHz. The flow field structures of the pulsed jet are connected with the time, with periodic wave structures generating in the flow field and moving downstream. The size of the wave structures and its distance are related to the frequency, namely the size and flow distance are relatively small at 50kHz, and it takes some time for the pulsed jet to establish its influence in the full flow field. At low frequencies, the flow field produces large fluctuations, and this may be detrimental to the stable operation of the engine.

This work aims to investigate experimentally the effect of the Reynolds number Re, based on the nozzle diameter D, on jet mixing manipulation using an unsteady radial minijet. A novel artificial intelligence (AI) control system has been developed to manipulate the jet over Re = 5800–40 000. The system may optimize simultaneously the control law and a time-independent parameter, which dictate the actuation ON/OFF states and amplitude, respectively. The control parameters include the mass flow rate, excitation frequency and diameter ratios (Cm, fe/f0 and d/D) of the minijet to the main jet as well as the duty cycle (α) of minijet injection. Jet mixing is quantified using Ke and K0, where K is the decay rate of the jet centreline mean velocity, and subscripts e and 0 denote the manipulated and unforced jets, respectively. It has been found that the maximum Ke achievable does not vary with Re. Scaling analysis of the huge volume of experimental data obtained from the AI system reveals that the relationship Ke = g1 (Cm, fe/f0, α, d/D, Re, K0) may be reduced to Ke/K0 = g2 $(\zeta )$, where g1 and g2 are different functions and the scaling factor $\zeta = ({C_m}/\alpha ){(D/d)^{1 - n}}(1/Re){({f_e}/{f_{e,opt}})^m}$, m and 1 − n are the power indices, and subscript opt denotes the value at which Ke is maximum. The scaling law is discussed in detail, along with the physical meanings of the dimensionless parameters Ke/K0, ζ, $({C_m}/\alpha ){(D/d)^{1 - n}}(1/Re)$ and ${({f_e}/{f_{e,opt}})^m}$.

A fast numerical method for unsteady aerodynamic calculation of 3D wing is established, which is suitable for the preliminary design. Based on the lifting-line method, the aerodynamic data of the 2D aerofoil obtained by the unsteady CFD simulation is used as the model input to solve the aerodynamic force of the 3D wing. Compared with the traditional steady lifting-line method, the augmented method adopts the unsteady Kutta-Jouowski (K-J) theorem to calculate the circulation and improve the accuracy of the method through the circulation correction. The pitching motion of 3D wing at different aspect ratio and reduction frequencies are studied. The results show that the aerodynamic forces obtained by the augmented lifting-line method have good agreement with the 3D unsteady CFD calculations. Compared with 3D CFD calculation, the calculation efficiency of the improved method is increased by more than 12 times. The improved method has extensive applicability and can be used to estimate the unsteady aerodynamic forces of 3D single or multiple wing configurations.

To investigate the downstream rim seal gas ingestion characteristics of a 1.5-stage turbine, the URANS equations were solved numerically using the SST turbulence model. The effects of different purge flow rates and the second vane on the ingestion characteristics of the aft cavity and the nonuniform fluctuations of the main gas path pressure are analysed. The results showed that the aft cavity is affected by the combined effects of the blade and the second vane, and the potential field at the leading edge of the second vane greatly influence the airflow variation in the aft cavity, which enhances the ingress of the mainstream into the wheel-space. The front purge flow weakens the egress between the suction side of the blade and the suction side of the second vane. The potential field at the leading edge of the second vane suppresses the nonuniform distribution of airflow in the aft cavity caused by the rotational effect of the blade.

The full-wing solar-powered UAV has a large aspect ratio, special configuration, and excellent aerodynamic performance. This UAV converts solar energy into electrical energy for level flight and storage to improve endurance performance. The UAV only uses a differential throttle for lateral control, and the insufficient control capability during crosswind landing results in a large lateral distance bias and leads to multiple landing failures. This paper analyzes 11 landing failures and finds that a large lateral distance bias at the beginning of the approach and the coupling of base and differential throttle control is the main reason for multiple landing failures. To improve the landing performance, a heading angle-based vector field (VF) method is applied to the straight-line and orbit paths following and two novel 3D Dubins landing paths are proposed to reduce the initial lateral control bias. The results show that the straight-line path simulation exhibits similar phenomenon with the practical failure; the single helical path has the highest lateral control accuracy; the left-arc to left-arc (L-L) path avoids the saturation of the differential throttle; and both paths effectively improve the probability of successful landing.

In this paper, the generation of relativistic electron mirrors (REM) and the reflection of an ultra-short laser off the mirrors are discussed, applying two-dimension particle-in-cell simulations. REMs with ultra-high acceleration and expanding velocity can be produced from a solid nanofoil illuminated normally by an ultra-intense femtosecond laser pulse with a sharp rising edge. Chirped attosecond pulse can be produced through the reflection of a counter-propagating probe laser off the accelerating REM. In the electron moving frame, the plasma frequency of the REM keeps decreasing due to its rapid expansion. The laser frequency, on the contrary, keeps increasing due to the acceleration of REM and the relativistic Doppler shift from the lab frame to the electron moving frame. Within an ultra-short time interval, the two frequencies will be equal in the electron moving frame, which leads to the resonance between laser and REM. The reflected radiation near this interval and corresponding spectra will be amplified due to the resonance. Through adjusting the arriving time of the probe laser, a certain part of the reflected field could be selectively amplified or depressed, leading to the selective adjustment of the corresponding spectra.

This report is on the synthesis by electrospinning of multiferroic core-shell nanofibers of strontium hexaferrite and lead zirconate titanate or barium titanate and studies on magneto-electric (ME) coupling. Fibers with well-defined core–shell structures showed the order parameters in agreement with values for nanostructures. The strength of ME coupling measured by the magnetic field-induced polarization showed the fractional change in the remnant polarization as high as 21%. The ME voltage coefficient in H-assembled films showed the strong ME response for the zero magnetic bias field. Follow-up studies and potential avenues for enhancing the strength of ME coupling in the core–shell nanofibers are discussed.

The majority of neuroimaging studies reported smaller hippocampal volumes in patients with posttraumatic stress disorder (PTSD). Our previous study found that PTSD is associated with selective volume loss of the CA3/dentate gyrus subfields However, the causality of smaller hippocampal volumes and PTSD cannot be determined in these studies because of the cross-sectional nature of them. The purpose of this longitudinal study was to determine if PTSD caused hippocampal subfields volume loss following traffic accidents. Volumes of hippocampal subfields in thirty seven traffic accident survivors were measured using 3T MRI in one week after accident, and twenty five of them completed one year follow-up MRI scan. Fourteen participants met the PTSD diagnosis in one year follow-up while other eleven did not met PTSD diagnosis criteria. PTSD was significantly associated with volumes reduction of CA3/dentate gyrus subfield (β=0.244 p=0.017) while other subfields were spared. It also shown volume loses of Entorhinal Cortex (ERC) of both side in one year follow-up for the whole sample (mean volume reduction: right 19.25mm3, left 22.04 mm3). But no association has been found between PTSD and ERC volume alteration. The findings indicate for the first time in humans that selective volume loss of the CA3/dentate gyrus subfields is the results but not the risk factor of PTSD. It also suggested that ERC may also be a stress sensitive region.