To enhance the operational management capabilities of general aviation airports, this paper proposes a method for evaluating the runway capacity of general aviation airports based on the runway expansion system. Firstly, it provides a brief introduction to the flight rules of general aviation airports and arrival and departure flight procedures with symmetrical characteristics, which serve as a theoretical basis for establishing the runway expansion system. Subsequently, a runway expansion system that covers symmetrical flight activities such as departure and arrival under a visual flight rule and an instrument flight rule is proposed, providing a conceptual model for evaluating the runway capacity of general aviation airports. On this foundation, the classical space–time analysis model is improved to establish a single runway arrival, departure, and mixed operation capacity evaluation model for general aviation airports. Finally, the reliability and rationality of this method are verified through case evaluations and three sets of numerical experiments with symmetrical relationships. The experiments demonstrate that this method can better reflect the actual conditions of the runways at general aviation airports while ensuring flight safety, and it can provide a reference for related research.

Faults in aero-engine rotating components account for more than 60% of total failures, and their early features are easily masked by noise under complex conditions. Traditional single-sensor diagnosis suffers from low feature utilization, poor interpretability, and weak cross-condition generalization. This paper proposes a multi-source fault diagnosis method for aero-engines based on an explainable boosted tree, integrating spatiotemporal attention (STA) and adaptive feature selection (AFS). We collect multi-domain data from four standard core sensors widely used in existing engine health management systems and extract multi-dimensional features to build a heterogeneous feature set. Adaptive feature selection is implemented using mutual information and a variance inflation factor. A spatiotemporal attention mechanism is introduced to weight and fuse features effectively. The fused features are used to train an XGBoost classifier, and SHAP values are adopted to quantify feature contributions and improve model interpretability. Uncertainty sources and sensitivity boundaries are quantitatively analyzed to support engineering acceptance. The method achieves high sensitivity to early weak faults and stable uncertainty under complex operating conditions. Tests on a fault simulation test rig show that the proposed method achieves 99.2% diagnosis accuracy and 97.5% cross-condition generalization accuracy, outperforming conventional models. It can identify early weak fault signatures, clarify key fault indicators, and provide a quantitative basis for fault tracing and maintenance decision-making. The method employs a standard sensor suite without additional hardware costs, features lightweight computation and low inference overhead, and delivers clear economic benefits by reducing false alarms, avoiding unplanned downtime, and optimizing maintenance resources. It offers a reliable, cost-effective solution for aero-engine fault diagnosis under complex operating conditions.

To address the fuzziness of multi-criteria evaluation information and the bounded rationality of decision-makers in small UAV procurement, this paper proposes a procurement decision-making method that incorporates the dual psychological aversion of regret and disappointment. Firstly, an evaluation index system for small UAV procurement suitable for border patrol missions is constructed, including endurance, communication reliability, wind resistance level, protection level, infrared detection range, and procurement cost. Secondly, intuitionistic fuzzy sets are employed to represent expert evaluation information, and an intuitionistic fuzzy weighted averaging operator is used to aggregate the evaluation information from multiple experts. On this basis, the intuitionistic fuzzy CRITIC method is applied to objectively determine the weights of the criteria. Subsequently, regret theory and disappointment theory are integrated to construct a comprehensive perceived utility function, which is then used to rank and select the candidate UAV models. Finally, a case study of a border patrol mission scenario is conducted to verify the feasibility and effectiveness of the proposed method. Sensitivity analysis demonstrates that within a reasonable range of psychological parameters, the core selection conclusions of the model remain highly robust.

Large-scale procurement of small UAVs to perform specific missions has become an important pathway to enhancing operational capability. To address the challenges of multi-objective conflicts, complex constraints, and large-scale variable coupling in procurement decision-making, this paper constructs a multi-objective procurement decision model covering system-of-systems operational effectiveness, life cycle cost, operational readiness rate, and allocation cycle, systematically incorporating practical hard constraints such as authorized establishment and allocation, security clearance, and domestic content rate. To efficiently solve this model, a multi-strategy fusion-based many-objective optimization algorithm (MSF-NSGA-III) is proposed. The algorithm designs a three-segment hybrid encoding and intelligent repair mechanism to ensure the feasibility of the solution space; integrates particle swarm learning and a strengthened dominance relation to enhance selection pressure in the high-dimensional objective space; and introduces adaptive crossover and mutation operators to balance the exploration and exploitation capabilities of the population. Simulations on large-scale instances demonstrate that MSF-NSGA-III outperforms the other five mainstream benchmark algorithms in core metrics such as hypervolume (HV) and inverted generational distance (IGD), and ablation experiments confirm that the proposed improvement strategies effectively enhance the convergence accuracy and distribution uniformity of the solution set. Case analysis verifies that the model can generate a high-quality non-dominated solution set and provide multi-objective balanced decision support for small UAV procurement; sensitivity analysis indicates that, within the tested parameter intervals, MSF-NSGA-III has low sensitivity to key parameters and exhibits good parameter robustness.