國防部軍備局中山科學研究院

Dibenzo-[18]-crown-6 (DB18C6) was used to form a novel cocrystal with ammonium dinitramide (ADN) via solvent evaporation under controlled temperature and humidity conditions to accelerate cocrystallization and reduce hygroscopicity. Single-crystal X-ray diffraction was applied for the validation of the ADN/DB18C6 cocrystal structure. This cocrystal's formation is primarily due to robust intermolecular hydrogen-bonding interactions mediated by the ammonium ions of ADN and the oxygen atoms present in the crown ether rings. In comparison with its individual components, the ADN/DB18C6 cocrystal shows an increased melting point (173 degrees C), which significantly exceeds those of ADN (90 degrees C) and DB18C6 (164 degrees C), suggesting enhanced thermal stability. Furthermore, under conditions of 30 degrees C with 60% relative humidity for a duration of 12 hours, the hygroscopicity of the ADN/DB18C6 cocrystal is noticeably reduced compared to that of pristine ADN. The moisture adsorption decreases from 20.60% for ADN to only 1.29% for the cocrystal. Tests for impact and friction sensitivity further reveal that the ADN/DB18C6 cocrystal has an impact sensitivity of 10 J, which is a bit elevated compared to that of raw ADN at 7.5 J, while its friction sensitivity exceeds 360 N, which is considerably greater than ADN's 84 N, thus significantly improving safety during the handling and storage of energetic materials. In summary, these findings show that cocrystallization serves as a successful approach for tuning and enhancing ADN's physicochemical characteristics, offering a feasible means to improve the safety and usability of ADN-based energetic materials.

Space-Time Adaptive Processing (STAP) remains limited by high computational cost and performance degradation in non-homogeneous environments. This paper proposes a novel NHD-Based Hole Training strategy for Σ∆-STAP (SD-STAP), tackling both challenges through two mechanisms: 1) A Non-Homogeneous Detector (NHD) is used to dynamically exclude contaminated training snapshots, effectively preventing target self-nulling in challenging multi-target scenarios. 2) A block-based structure significantly reduces computational complexity. RFView simulations confirm the NHD’s robustness and SINR improvement. Comparing four strategies, the proposed SD-NHD-Base-Hole achieved the best performance-complexity trade-off, demonstrating a near order-of-magnitude reduction in theoretical complexity and confirming its feasibility for real-time STAP radar systems.

Emergency Medical Services (EMS) require timely and equitable ambulance allocation supported by accurate demand estimation. In our prior work, we developed a statistical forecasting module based on Overall Smoothed Average Demand (OSAD) and Average Maximum (AMX) to estimate proportional EMS demand across spatial zones. Although this approach was interpretable and computationally efficient, it was limited in modeling nonlinear spatiotemporal dependencies and adapting to dynamic demand variations. This paper presents a unified deep learning-based EMS planning framework that integrates spatiotemporal demand forecasting with adaptive ambulance allocation. Specifically, the statistical OSAD/AMX estimators are replaced by graph-based spatiotemporal forecasting models capable of capturing spatial interactions and temporal dynamics. The predicted demand is then incorporated into a reinforcement learning-based allocator that dynamically optimizes ambulance placement under fairness, coverage, and operational constraints. Experiments conducted on real-world EMS datasets demonstrate that the proposed end-to-end framework not only improves demand forecasting accuracy but also translates these improvements into tangible operational benefits, including enhanced equity in resource distribution and reduced response distance. Compared with traditional statistical and heuristic-based baselines, the proposed approach provides a more adaptive and decision-aware solution for EMS planning.

To address the high experimental costs and data scarcity inherent in Directed Energy Deposition (DED), this study proposes a data-efficient hybrid optimization framework for the precision manufacturing of Inconel 718 aerospace components. The framework leverages a two-stage strategy to bridge traditional experimental design with advanced machine learning, ensuring robust process optimization even with limited datasets. In the first stage, the Taguchi method (L16 orthogonal array) was employed for coarse-grained screening to identify influential control factors. In the second stage, a Fully Connected Neural Network (FNN) coupled with Bayesian Optimization (BO) was deployed. Crucially, this machine learning component functions as an optimization-oriented trend surrogate rather than a global regressor, successfully guiding the optimization under extreme data scarcity. The optimized process window yielded exceptional structural integrity, achieving a porosity as low as 0.03%. To thoroughly validate its practical efficacy, tensile testing (ASTM E8/E8M) and Rockwell hardness measurements (ASTM E18) were systematically conducted on the optimized specimens. The mechanical characterization demonstrated an average tensile strength of approximately 1358 MPa and a hardness of similar to 40 HRC. Finally, the framework was successfully validated through the robotic DED fabrication of a complex-geometry aerospace engine combustion chamber casing, bridging laboratory-scale optimization with authentic industrial applications.

This study investigates the reinforcement of AZ31B magnesium alloy with bimodal (micron-and nano-sized) Ti particles. The influence of Ti content (0, 3, 6, and 9 vol%) on solidification behavior, ECAP-induced microstructural evolution, dynamic recrystallization (DRX), twinning, texture development, and resulting mechanical and electrochemical properties was examined. In the as-cast state, the addition of Ti particles transforms the beta-phase from a continuous network into discrete particles and weakens the basal texture, a result of heterogeneous nucleation and solute redistribution at the solidification front. Following ECAP, all composites exhibit extensive DRX; however, the homogeneity of grain refinement is strongly composition-dependent. The 6 vol% Ti composite displays the finest, most uniform equiaxed grains, accompanied by the weakest basal texture and highest orientation randomization. Notably, the 6 vol% Ti composite features a distinct banded region of ultra-fine DRX grains. This microstructure arises from a synergistic mechanism: micron-sized particles induce localized high strain gradients and particle-stimulated nucleation (PSN), while nano-sized particles provide robust Zener pinning to stabilize the refined grains. Zener parameter analysis confirms that grain boundary pinning is predominantly governed by the nano-sized fraction, whereas micron-sized particles facilitate DRX activation and twinning. Crucially, the efficacy of these mechanisms hinges on particle dispersion, which is optimized in the 6 vol% Ti composite. Consequently, this composition achieves a superior strength-ductility balance and enhanced corrosion resistance, characterized by reduced current density and uniform degradation. Overall,the 6 vol% Ti addition offers the optimal trade-off among grain refinement, texture weakening, and multi-functional property enhancement in the AZ31B alloy.