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.

To investigate the advantages and disadvantages of two multi-swirl fuel-rich dome configurations, namely the triple-swirler and double-swirler, for a novel high-temperature rise centre-staged combustor, this study employed ANSYS Fluent software. Utilising the Reynolds-averaged Navier-Stokes (RANS) equation as the governing equation, three-dimensional numerical simulations were conducted using the Realisable k-ε turbulence model and non-premixed probability density function (PDF) combustion model to analyse the flow and combustion characteristics of both configurations. A comparative study was then performed to evaluate the performance differences between the two dome configurations under take-off and idle conditions. The results demonstrate that, under both conditions, the fuel-air mixing in the triple-swirler combustor occurs faster and more uniformly. Specifically, during takeoff, the primary zone temperature distribution in the triple-swirler combustor is more uniform, while during idle, the fuel-rich combustion region is more symmetrical. Furthermore, across both conditions, the outlet temperature distribution of the triple-swirler combustor is of superior quality, albeit with equivalent combustion efficiency. Notably, the formation of NOx and soot in the triple-swirler combustor, during takeoff conditions, exceeds that of the double-stage combustor along the flow path, whereas the generation of CO and UHC, during idle conditions, is lower in the former.

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.

The present work investigates the thermochemical non-equilibrium effect in the DLR combustor using a two-temperature model combined with vibration-chemistry coupling model. Two operating conditions with inflow Mach 2 and 6 are selected for study. The simulation results illustrate that translational-vibrational non-equilibrium is related to energy transfer behaviour and the translational-vibrational relaxation time. When kinetic energy and chemical energy are converted into internal energy, there is a significant difference in the degree of conversion to translational and vibrational energy. If the translational-vibrational relaxation time is larger than the flow time, such as the relaxation time of the mainstream aftershock wave is 0.25 s for the condition with inflow Mach 2, and the flow time is 3 × 10−5 s, non-equilibrium will occur. Significant differences exist between the flow fields with Mach 2 and 6. A clear boundary layer separation occurs at Mach 6. Combustion occurs at the shear layer, which is in translational-vibrational equilibrium, and there are varying degrees of non-equilibrium in other locations. The dissociation of N2 and production of NO primarily occur on the strut walls and the upper/lower walls of the combustor. The mass fraction of NO is higher than the value at Mach 2. The combustion performance is influenced by the thermochemical non-equilibrium effect. At the condition of Mach 2, it increases the combustion efficiency by 10% near the injector and 0.27% at outlet relatively. Non-equilibrium inhibits the initial upstream combustion while slightly promoting downstream combustion under inflow Mach 6 condition.

The interaction of helminth infections with type 2 diabetes (T2D) has been a major area of research in the past few years. This paper, therefore, focuses on the systematic review of the effects of helminthic infections on metabolism and immune regulation related to T2D, with mechanisms through which both direct and indirect effects are mediated. Specifically, the possible therapeutic role of helminths in T2D management, probably mediated through the modulation of host metabolic pathways and immune responses, is of special interest. This paper discusses the current possibilities for translating helminth therapy from basic laboratory research to clinical application, as well as existing and future challenges. Although preliminary studies suggest the potential for helminth therapy for T2D patients, their safety and efficacy still need to be confirmed by larger-scale clinical studies.

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.

The impact of the self-sealing band on interior ballistics is investigated during the gun launching, and a high-precision interior ballistics coupling algorithm that takes leakage into account is proposed. This study focuses on a 65 mm short-barrel, equal-caliber balanced cannon, integrating Abaqus finite element software with an interior ballistics calculation programme. It uses a User-defined AMPlication Load (VUAMP) subroutine to achieve real-time coupling calculations of the chamber pressure and self-sealing band deformation, correcting variations in the chamber pressure. Experimental results show that the coupling algorithm offers the higher precision compared to traditional interior ballistics models and can effectively capture the impact of leakage on the interior ballistics performance. Further research reveals that changes in the charge amount and assembly gap significantly affect the sealing performance of the self-sealing band and the leakage of propellant gases, which in turn influence the chamber pressure and projectile velocity. The high-precision coupling algorithm proposed in this paper provides the effective theoretical support for the design of the self-sealing band and the analysis of cannon performance.

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.

Background: Meningiomas are the most common intracranial tumors. Radiotherapy (RT) serves as an adjunct following surgical resection; however, response varies. RTOG-0539 is a prospective, phase 2, trial that stratified patients risk groups based on clinical and pathological criteria, providing key benchmarks for RT outcomes. This is the first study that aims to characterize the molecular landscape of an RT clinical trial in meningiomas. Methods: Tissue from 100 patients was analyzed using DNA methylation, RNA sequencing, and whole-exome sequencing. Copy number variations and mutational profiles were assessed to determine associations with meningioma aggressiveness. Tumors were molecularly classified and pathway analyses were conducted to identify biological processes associated with RT response. Results: High-risk meningiomas exhibited cell cycle dysregulation and hypermetabolic pathway upregulation. 1p loss and 1q gain were more frequent in aggressive meningiomas, and NF2 and non-NF2 mutations co-occurred in some high-risk tumors. Molecular findings led to the reclassification of several cases, highlighting the limitations of histopathologic grading alone. Conclusions: This is the first study to comprehensively characterize the molecular landscape of any RT trial in meningioma, integrating multi-omic data to refine treatment stratification. Findings align with ongoing genomically driven meningioma clinical trials and underscore the need for prospective tissue banking to enhance biomarker-driven treatment strategies.

Background: The WHO grade of meningioma was updated in 2021 to include homozygous deletions of CDKN2A/B and TERT promotor mutations. Previous work including the recent cIMPACT-NOW statement have discussed the potential value of including chromosomal copy number alterations to help refine the current grading system. Methods: Chromosomal copy number profiles were inferred from from 1964 meningiomas using DNA methylation. Regularized Cox regresssion was used to identify CNAs independenly associated with post-surgical and post-RT PFS. Outcomes were stratified by WHO grade and novel CNAs to assess their potential value in WHO critiera. Results: Patients with WHO grade 1 tumours and chromosome 1p loss had similar outcomes to those with WHO grade 2 tumours (median PFS 5.83 [95% CI 4.36-Inf] vs 4.48 [4.09-5.18] years). Those with chromosome 1p loss and 1q gain had similar outcomes to those with WHO grade 3 cases regardless of initial grade (median PFS 2.23 [1.28-Inf] years WHO grade 1, 1.90 [1.23-2.25] years WHO grade 2, compared to 2.27 [1.68-3.05] years in WHO grade 3 cases overall). Conclusions: We advocate for chromosome 1p loss being added as a criterion for a CNS WHO grade of 2 meningioma and addition of 1q gain as a criterion for a CNS WHO grade of 3.

Background: We previously developed a DNA methylation-based risk predictor for meningioma, which has been used locally in a prospective fashion. As a follow-up, we validate this model using a large prospective cohort and introduce a streamlined next-generation model compatible with newer methylation arrays. Methods: The performance of our next-generation predictor was compared with our original model and standard-of-care 2021 WHO grade using time-dependent receiver operating characteristic curves. A nomogram was generated by incorporating our methylation predictor with WHO grade and extent of resection. Results: A total of 1347 meningioma cases were utilized in the study, including 469 prospective cases from 3 institutions and a retrospective cohort of 100 WHO grade 2 cases for model validation. Both the original and next-generation models significantly outperformed 2021 WHO grade in predicting postoperative recurrence. Dichotomizing into grade-specific risk subgroups was predictive of outcome within both WHO grades 1 and 2 tumours (log-rank p<0.05). Multivariable Cox regression demonstrated benefit of adjuvant radiotherapy in high-risk cases specifically, reinforcing its informative role in clinical decision making. Conclusions: This next-generation DNA methylation-based meningioma outcome predictor significantly outperforms 2021 WHO grading in predicting time to recurrence. This will help improve prognostication and inform patient selection for RT.

Background: Meningiomas exhibit considerable heterogeneity. We previously identified four distinct molecular groups (immunogenic, NF2-wildtype, hypermetabolic, proliferative) which address much of this heterogeneity. Despite their utility, the stochasticity of clustering methods and the requirement of multi-omics data limits the potential for classifying cases in the clinical setting. Methods: Using an international cohort of 1698 meningiomas, we constructed and validated a machine learning-based molecular classifier using DNA methylation alone. Original and newly-predicted molecular groups were compared using DNA methylation, RNA sequencing, whole exome sequencing, and clinical outcomes. Results: Group-specific outcomes in the validation cohort were nearly identical to those originally described, with median PFS of 7.4 (4.9-Inf) years in hypermetabolic tumors and 2.5 (2.3-5.3) years in proliferative tumors (not reached in the other groups). Predicted NF2-wildtype cases had no NF2 mutations, and 51.4% had others mutations previously described in this group. RNA pathway analysis revealed upregulation of immune-related pathways in the immunogenic group, metabolic pathways in the hypermetabolic group and cell-cycle programs in the proliferative group. Bulk deconvolution similarly revealed enrichment of macrophages in immunogenic tumours and neoplastic cells in hypermetabolic/proliferative tumours. Conclusions: Our DNA methylation-based classifier faithfully recapitulates the biology and outcomes of the original molecular groups allowing for their widespread clinical implementation.