The realization of high-performance electromagnetic wave (EMW) absorbing materials is particularly dependent on the regulation of interface structures, and the construction of multi-component heterogeneous interfaces is an effective strategy to achieve high-performance EMW absorption in materials. In this study, a three-dimensional flower-like ZnS/Co1 -x S@C composite material was prepared by hydrothermal method, stepwise gas-phase selenization, and gas-phase sulfidation processes. The formation of multiple heterogeneous interfaces through stepwise gas-phase selenization and sulfidation enhanced the interface polarization ability of the material and significantly improved the electromagnetic wave loss efficiency. The results show that the ZnS/Co1 -x S@C composite material has excellent EMW absorption performance, with a minimum reflection loss (RLmin ) of -53.75 dB at 3.4 mm and a maximum effective absorption bandwidth (EABmax ) of 7.28 GHz at 3.2 mm. Density functional theory (DFT) calculations verified that the built-in electric field constructed at the heterogeneous interface strengthened the interface polarization loss. Meanwhile, the radar cross-section (RCS) simulation further demonstrated that the material has good electromagnetic stealth properties, providing a new idea for the research on the construction of multiple heterogeneous interface sulfide composites. (c) 2026 Published by Elsevier Ltd on behalf of The editorial office of Journal of Materials Science & Technology.
Finite element (FE) methods are the benchmark for solid mechanics simulations, yet their computational cost becomes prohibitive in problems exhibiting localized nonlinearities, fine-scale features, or long-time dynamic evolution. In our earlier FE-neural operator (FE-NO) hybrid framework (Wang et al., 2025), physics-informed deep operator networks were coupled with FE solvers through overlapping domain decomposition with Dirichlet–Dirichlet interface exchange, accelerating computationally intensive subdomains employing NOs while preserving FE fidelity elsewhere. Two limitations remained: the overlapping formulation required redundant interface computations that increased inner Schwarz iteration counts, and the convolutional feature extractor restricted the NO subdomain to structured grids, precluding irregular geometries. This work addresses both limitations. A non-overlapping Schwarz alternating method with Neumann–Dirichlet interface exchange replaces the overlapping formulation, transmitting traction boundary conditions from the NO to FE rather than exchanging displacement. This eliminates the overlap layer and reduces the number of inner Schwarz iterations, while maintaining bounded error accumulation across all tested time horizons. To enable simulations on arbitrarily shaped subdomains, a Point-DeepONet is introduced, which directly operates on unstructured FE point clouds without interpolation, extending the framework to non-convex and irregular subdomain geometries. Furthermore, strain and stress operators are derived analytically from the displacement operators through the governing kinematic equations, rather than trained as independent networks, significantly reducing the number of trainable parameter sets, while enforcing mechanical consistency by construction. The framework is validated on three solid mechanics benchmarks: static linear elasticity, quasi-static hyperelasticity, and elastodynamics with both regular and irregular subdomain geometries. These results establish a non-overlapping FE-NO coupling paradigm that is geometry-flexible, parameter-efficient, and convergence-stable, providing a principled pathway for hybrid physics-based and operator-learning solvers in large-scale dynamic solid mechanics.
The extracellular matrix (ECM) undergoes extensive architectural remodeling during tumor progression, generating nanoscale alignment cues that profoundly influence behaviors of immune cells. However, whether ECM nanoarchitecture directly programs their immune secretory functions remains poorly defined. Here, we develop a magnetically switchable hydrogel that enables reversible, on-demand modulation of nanoscale ligand alignment without altering bulk stiffness or composition. Using this platform, we uncover a mechanotransduction pathway in tumor-associated macrophages (TAMs) whereby matrix nanoalignment enhances integrin β1-dependent mechanosensing and transient receptor potential cation channel subfamily V member 2 (TRPV2)-dependent Ca2+ signaling, thereby promoting phosphoinositide 3-kinase (PI3K)–Ras-related protein 11a (Rab11a)-associated small extracellular vesicle (sEV) release by approximately tenfold. Beyond increasing vesicle output, PI3K–mechanistic target of rapamycin complex 2-associated signaling altered the particle-normalized cargo profile of sEVs, with preferential enrichment of immunoregulatory molecules, including M2-promoting cytokines and miR-181a-5p. These sEVs reprogram macrophages toward an immunosuppressive M2-like phenotype, suppress cytotoxic CD8+ T-cell infiltration, and accelerate melanoma progression in vivo. Importantly, selective disruption of the TRPV2–Rab11a axis or pharmacological attenuation of ECM remodeling restores antitumor immunity and restricts tumor growth. Together, our findings demonstrate that ECM nanoarchitecture is sufficient to regulate macrophage sEV programming in a defined matrix environment and identify TRPV2-associated mechanotransduction as a potential therapeutic vulnerability in matrix-remodeled tumors.
Fairness in same-day delivery (SDD) becomes increasingly important as customers demand equitable service pledge, yet cost-driven merchants’ preferences for nearby customers create service disparities across regions. Drone fleet resupply presents a fast and traffic-free solution capable of navigating complex terrains, while truck-based delivery complements it by overcoming the drone’s limitations in endurance and operational range. Previous studies mainly address single-drone, single-truck problems, with limited attention to the potential of drone fleet resupply with multi-truck systems to solve fairness issues. This research fills this gap by originally proposing a drone fleet resupply system for stochastic requests, where drones perform multiple trips to replenish any truck as needed, with consideration of drone endurance limitations. This problem is formulated as a sequential decision process (SDP) and solved through a deep Q-learning (DQL) approach to maximize service rates while ensuring fairness. A piecewise linear reward function is designed to improve convergence. Comparative results against benchmark policies demonstrate DQL’s potential to manage highly stochastic logistics operations. Key findings include: (1) Drone fleet resupply enhances regional fairness and overall service rates, demonstrating strong robustness in serving long-distance and time-sensitive deliveries; (2) An appropriate reward function significantly facilitates fairness, while penalties further improve it but are ineffective alone; (3) An optimal drone-truck fleet mix exists to balance service rates and associated costs, while adding more trucks alone substantially improves service rates but incurs larger costs; (4) The proposed approach remains effective across drone capacities, while extended drone endurance yields limited gains in service rate but significantly lowers delivery costs.
Ignition delay time (IDT) measurements in rapid compression machines (RCMs) are inherently distorted by facility-specific transient thermal effects, leading to significant data discrepancies. Existing correction strategies either rely on “effective volume” simulations or require multi-condition extrapolations, limiting their direct applicability to standalone experimental datasets. To address this, a model-independent correction method that inverts the Livengood-Wu integral along experimentally measured thermal trajectories to recover idealized steady-state IDT correlations has been proposed. The Arrhenius-type correlation parameters were optimized by minimizing the mean squared error in logarithmic space between the predicted ignition timing (integral = 1) and measured values. Validated against ethanol autoignition data from five independent RCMs (20–40 bar, diluted/air conditions), the method reduces the coefficient of variation from 20 to 34% to 5–14%, achieving good agreement with zero-dimensional adiabatic constant-volume simulations. Uncertainty propagation analysis reveals that the correction is robust against pressure fluctuations (±5%) while linearly translating temperature uncertainties into the corrected IDTs. This framework has established a direct, quantitative link between non-ideal transient measurements and idealized steady-state IDT correlations, significantly simplifying cross-facility data comparison and enhancing the utility of RCM datasets for kinetic mechanism simulations.
