We prove that the N-solitons, including breathers and multi-hump solitons, of the coupled nonlinear Schr & ouml;dinger (CNLS) equations are orbitally stable in the Sobolev space HN(& Ropf;; & Copf;2). Moreover, (N1, N2)-solitons of the coupled modified Korteweg-de Vries (CmKdV) equations are shown to be orbitally stable in the Sobolev space H2N1+N2(& Ropf;; & Ropf;2). The number of negative eigenvalues of the second variation of the Lyapunov functional is N for N-solitons of the CNLS equations, and N1 + & LeftFloor;(N2 + 1)/2 & RightFloor; for (N1, N2)-solitons of the CmKdV equations, which is obtained by exploiting integrable properties. The stability of solitons for the classical NLS and mKdV equations also follows from the same method. In addition, we show that solutions to the linearized spectral problem of the associated mixed flow equation can be constructed from solutions of the stationary zero curvature equations in a large class of Lie algebras. (c) 2026 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
Zero-Shot Composed Image Retrieval (ZS-CIR) aims to retrieve images by combining a reference image with a textual description, without requiring supervision from target images. While existing self-supervised methods fuse visual and textual representations, they often struggle to capture fine-grained visual details and lack robust cross-modal interactions due to inadequate alignment supervision. To address this, we propose a novel framework, Dual-granularity Image-Text Alignment (DITA). DITA employs an Adaptive Token Pooling (ATP) module to compress visual tokens into an informative set, preserving rich image details for effective text querying while eliminating redundancy. It then introduces two core contrastive objectives: (i) an image-only prompt, formed by combining template text with the pooled tokens, aligns with the reference image for its cross-modal self-awareness, and (ii) a masked composite prompt, where a caption with masked keywords is appended to the image-only prompt, interacts with synthetic hard descriptions for image-text alignment and promotes their fine-grained fusion. To rigorously evaluate DITA, we conduct extensive experiments on FashionIQ, CIRR, and CIRCO benchmarks using diverse backbones (e.g., BLIP, BLIP-2, SigLIP). The experimental results demonstrate that DITA consistently outperforms competitive self-supervised baselines. Furthermore, DITA exhibits exceptional robustness against adversarial queries and varying text complexities. With both BLIP-based backbones, DITA processes each query in about 14 ms on a single RTX 4090 GPU. Compared with OSrCIR, a representative LLM-based training-free method relying on API calls, DITA achieves about 40x lower online latency while maintaining comparable retrieval performance. These results confirm that DITA provides a scalable, robust, and highly efficient solution for real-world ZS-CIR applications.
A two-step microarc oxidation (MAO) fabricates in situ Ce-doped coatings with self-sealing and self-healing capabilities on the magnesium (Mg) alloy. The CePO4/MgO composite melt effectively seals discharge pores, while its nanocrystalline-amorphous structure enhances coating densification and corrosion resistance. Electrochemical and salt solution demonstrate that Ce-doped coatings exhibit excellent corrosion resistance. During corrosion, released Ce3+ promotes formation of Ce(OH)3/Mg(OH)2 products. Density functional theory (DFT) calculations reveal these products exhibit increased work function and weakened Cl− adsorption, synergistically forming a robust electronic barrier that suppresses corrosive attack and enables persistent self-healing protection.
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.
With the increasing deployment of Cyber-Physical Systems (CPS), the risk landscape in chemical industrial parks has progressively shifted from the physical domain to the cyber domain. This paradigm shift enables cyber adversaries to manipulate physical processes, thereby inducing catastrophic accidents. Traditional Industrial Control Systems (ICS) defense methods often isolate cyber vulnerability analysis from physical consequence analysis, failing to address this cross-domain threat. To bridge this gap, a hierarchical risk transmission model is established based on a three-layer architecture of chemical park CPS, spanning the information, cyber-physical, and physical layers. Attack graphs are utilized to quantify cross-layer attack paths, and Bayesian networks are employed to infer the severity of physical consequences. A coupling index is proposed to prioritize node defense resources under limited budgets. Furthermore, an attack-defense game model solved by a greedy algorithm is developed to realize a honeypot deployment scheme. The framework is validated using both a real cascaded water tank experimental platform and the non-linear Tennessee Eastman Process benchmark. Results demonstrate that the proposed method effectively intercepts unknown attacks originating from the information layer, thereby preventing catastrophic physical intrusions.
